gretl-common 1.9.14-2 / usr / share / gretl / gretlcli.hlp

headings 10
Tests 22
add
adf
chow
coeffsum
coint
coint2
cusum
difftest
hausman
kpss
leverage
levinlin
meantest
modtest
normtest
omit
qlrtest
reset
restrict
runs
vartest
vif
Graphs 7
boxplot
gnuplot
graphpg
qqplot
rmplot
scatters
textplot
Statistics 13
anova
corr
corrgm
fractint
freq
hurst
mahal
pca
pergm
spearman
summary
xcorrgm
xtab
Dataset 18
append
data
dataset
delete
genr
info
join
labels
markers
nulldata
open
rename
setinfo
setobs
setmiss
smpl
store
varlist
Estimation 34
ar
ar1
arbond
arch
arima
biprobit
dpanel
duration
equation
estimate
garch
gmm
heckit
hsk
intreg
kalman
lad
logistic
logit
mle
mpols
negbin
nls
ols
panel
poisson
probit
quantreg
system
tobit
tsls
var
vecm
wls
Programming 17
break
catch
clear
debug
elif
else
end
endif
endloop
foreign
function
if
include
loop
makepkg
run
set
Transformations 9
diff
discrete
dummify
lags
ldiff
logs
orthdev
sdiff
square
Printing 7
eqnprint
modprint
outfile
print
printf
sprintf
tabprint
Prediction 1
fcast
Utilities 5
help
modeltab
pvalue
quit
shell

# add Tests

Argument:   varlist 
Options:    --lm (do an LM test, OLS only)
            --quiet (print only the basic test result)
            --silent (don't print anything)
            --vcv (print covariance matrix for augmented model)
            --both (IV estimation only, see below)
Examples:   add 5 7 9
            add xx yy zz --quiet

Must be invoked after an estimation command. Performs a joint test for the
addition of the specified variables to the last model, the results of which
may be retrieved using the accessors $test and $pvalue.

By default an augmented version of the original model is estimated,
including the variables in varlist. The test is a Wald test on the augmented
model, which replaces the original as the "current model" for the purposes
of, for example, retrieving the residuals as $uhat or doing further tests.

Alternatively, given the --lm option (available only for the models
estimated via OLS), an LM test is performed. An auxiliary regression is run
in which the dependent variable is the residual from the last model and the
independent variables are those from the last model plus varlist. Under the
null hypothesis that the added variables have no additional explanatory
power, the sample size times the unadjusted R-squared from this regression
is distributed as chi-square with degrees of freedom equal to the number of
added regressors. In this case the original model is not replaced.

The --both option is specific to two-stage least squares: it specifies that
the new variables should be added both to the list of regressors and the
list of instruments, the default in this case being to add to the regressors
only.

Menu path:    Model window, /Tests/Add variables

# adf Tests

Arguments:  order varlist 
Options:    --nc (test without a constant)
            --c (with constant only)
            --ct (with constant and trend)
            --ctt (with constant, trend and trend squared)
            --seasonals (include seasonal dummy variables)
            --gls (de-mean or de-trend using GLS)
            --verbose (print regression results)
            --quiet (suppress printing of results)
            --difference (use first difference of variable)
            --test-down[=criterion] (automatic lag order)
Examples:   adf 0 y
            adf 2 y --nc --c --ct
            adf 12 y --c --test-down
            See also jgm-1996.inp

The options shown above and the discussion which follows pertain to the use
of the adf command with regular time series data. For use of this command
with panel data please see below.

Computes a set of Dickey-Fuller tests on each of the the listed variables,
the null hypothesis being that the variable in question has a unit root.
(But if the --difference flag is given, the first difference of the variable
is taken prior to testing, and the discussion below must be taken as
referring to the transformed variable.)

By default, two variants of the test are shown: one based on a regression
containing a constant and one using a constant and linear trend. You can
control the variants that are presented by specifying one or more of the
option flags.

In all cases the dependent variable is the first difference of the specified
variable, y, and the key independent variable is the first lag of y. The
model is constructed so that the coefficient on lagged y equals the root in
question minus 1. For example, the model with a constant may be written as

  (1 - L)y(t) = b0 + (a-1)y(t-1) + e(t)

Under the null hypothesis of a unit root the coefficient on lagged y equals
zero; under the alternative that y is stationary this coefficient is
negative.

If the lag order, k, is greater than 0, then k lags of the dependent
variable are included on the right-hand side of the test regressions. If the
order is given as -1, k is set following the recommendation of Schwert
(1989), namely 12(T/100)^0.25, where T is the sample size. In either case,
however, if the --test-down option is given then k is taken as the maximum
lag and the actual lag order used is obtained by testing down. The criterion
for testing down can be selected using the option parameter, which must be
one of MAIC, MBIC or tstat. The MAIC and MBIC methods are as described in Ng
and Perron (2001); the lag order is chosen so as to optimize an
appropriately modified version of the Akaike Information Criterion or the
Schwartz Bayesian Criterion, respectively. The MAIC method is the default
when no method is explicitly specified. The tstat method is a follows:

1. Estimate the Dickey-Fuller regression with k lags of the dependent
   variable.

2. Is the last lag significant? If so, execute the test with lag order k.
   Otherwise, let k = k - 1; if k equals 0, execute the test with lag order
   0, else go to step 1.

In the context of step 2 above, "significant" means that the t-statistic for
the last lag has an asymptotic two-sided p-value, against the normal
distribution, of 0.10 or less.

The --gls option can be used in conjunction with one or other of the flags
--c and --ct (the model with constant, or model with constant and trend).
The effect of this option is that the de-meaning or de-trending of the
variable to be tested is done using the GLS procedure suggested by Elliott,
Rothenberg and Stock (1996), which gives a test of greater power than the
standard Dickey-Fuller approach. This option is not compatible with --nc,
--ctt or --seasonals.

P-values for the Dickey-Fuller tests are based on MacKinnon (1996). The
relevant code is included by kind permission of the author. In the case of
the test with linear trend using GLS these P-values are not applicable;
critical values from Table 1 in Elliott, Rothenberg and Stock (1996) are
shown instead.

Panel data

When the adf command is used with panel data, to produce a panel unit root
test, the applicable options and the results shown are somewhat different.

First, while you may give a list of variables for testing in the regular
time-series case, with panel data only one variable may be tested per
command. Second, the options governing the inclusion of deterministic terms
become mutually exclusive: you must choose between no-constant, constant
only, and constant plus trend; the default is constant only. In addition,
the --seasonals option is not available. Third, the --verbose option has a
different meaning: it produces a brief account of the test for each
individual time series (the default being to show only the overall result).

The overall test (null hypothesis: the series in question has a unit root
for all the panel units) is calculated in one or both of two ways: using the
method of Im, Pesaran and Shin (Journal of Econometrics, 2003) or that of
Choi (Journal of International Money and Finance, 2001).

Menu path:    /Variable/Unit root tests/Augmented Dickey-Fuller test

# anova Statistics

Arguments:  response treatment [ block ] 
Option:     --quiet (don't print results)

Analysis of Variance: response is a series measuring some effect of interest
and treatment must be a discrete variable that codes for two or more types
of treatment (or non-treatment). For two-way ANOVA, the block variable
(which should also be discrete) codes for the values of some control
variable.

Unless the --quiet option is given, this command prints a table showing the
sums of squares and mean squares along with an F-test. The F-test and its
P-value can be retrieved using the accessors $test and $pvalue respectively.

The null hypothesis for the F-test is that the mean response is invariant
with respect to the treatment type, or in words that the treatment has no
effect. Strictly speaking, the test is valid only if the variance of the
response is the same for all treatment types.

Note that the results shown by this command are in fact a subset of the
information given by the following procedure, which is easily implemented in
gretl. Create a set of dummy variables coding for all but one of the
treatment types. For two-way ANOVA, in addition create a set of dummies
coding for all but one of the "blocks". Then regress response on a constant
and the dummies using "ols". For a one-way design the ANOVA table is printed
via the --anova option to ols. In the two-way case the relevant F-test is
found by using the "omit" command. For example (assuming y is the response,
xt codes for the treatment, and xb codes for blocks):

	# one-way
	list dxt = dummify(xt)
	ols y 0 dxt --anova
	# two-way
	list dxb = dummify(xb)
	ols y 0 dxt dxb
	# test joint significance of dxt
	omit dxt --quiet

Menu path:    /Model/Other linear models/ANOVA

# append Dataset

Argument:   filename 
Option:     --time-series (see below)

Opens a data file and appends the content to the current dataset, if the new
data are compatible. The program will try to detect the format of the data
file (native, plain text, CSV, Gnumeric, Excel, etc.).

The appended data may take the form of either additional observations on
variables already present in the dataset, or new variables. in the case of
adding variables, compatibility requires either (a) that the number of
observations for the new data equals that for the current data, or (b) that
the new data carries clear observation information so that gretl can work
out how to place the values.

A special feature is supported for appending to a panel dataset. Let n
denote the number of cross-sectional units in the panel, T denote the number
of time periods, and m denote the number of observations for the new data.
If m = n the new data are taken to be time-invariant, and are copied into
place for each time period. On the other hand, if m = T the data are treated
as non-varying across the panel units, and are copied into place for each
unit. If the panel is "square", and m equals both n and T, an ambiguity
arises. The default in this case is to treat the new data as time-invariant,
but you can force gretl to treat the new data as time series via the
--time-series option. (This option is ignored in all other cases.)

See also "join" for more sophisticated handling of multiple data sources.

Menu path:    /File/Append data

# ar Estimation

Arguments:  lags ; depvar indepvars 
Option:     --vcv (print covariance matrix)
Example:    ar 1 3 4 ; y 0 x1 x2 x3

Computes parameter estimates using the generalized Cochrane-Orcutt iterative
procedure; see Section 9.5 of Ramanathan (2002). Iteration is terminated
when successive error sums of squares do not differ by more than 0.005
percent or after 20 iterations.

"lags" is a list of lags in the residuals, terminated by a semicolon. In the
above example, the error term is specified as

  u(t) = rho(1)*u(t-1) + rho(3)*u(t-3) + rho(4)*u(t-4)

Menu path:    /Model/Time series/Autoregressive estimation

# ar1 Estimation

Arguments:  depvar indepvars 
Options:    --hilu (use Hildreth-Lu procedure)
            --pwe (use Prais-Winsten estimator)
            --vcv (print covariance matrix)
            --no-corc (do not fine-tune results with Cochrane-Orcutt)
Examples:   ar1 1 0 2 4 6 7
            ar1 y 0 xlist --pwe
            ar1 y 0 xlist --hilu --no-corc

Computes feasible GLS estimates for a model in which the error term is
assumed to follow a first-order autoregressive process.

The default method is the Cochrane-Orcutt iterative procedure; see for
example section 9.4 of Ramanathan (2002). Iteration is terminated when
successive estimates of the autocorrelation coefficient do not differ by
more than 0.001 or after 20 iterations.

If the --pwe option is given, the Prais-Winsten estimator is used. This
involves an an iteration similar to Cochrane-Orcutt; the difference is that
while Cochrane-Orcutt discards the first observation, Prais-Winsten makes
use of it. See, for example, Chapter 13 of Greene's Econometric Analysis
(2000) for details.

If the --hilu option is given, the Hildreth-Lu search procedure is used. The
results are then fine-tuned using the Cochrane-Orcutt method, unless the
--no-corc flag is specified. The --no-corc option is ignored for estimators
other than Hildreth-Lu.

Menu path:    /Model/Time series/AR(1)

# arbond Estimation

Argument:   p [ q ] ; depvar indepvars [ ; instruments ] 
Options:    --quiet (don't show estimated model)
            --vcv (print covariance matrix)
            --two-step (perform 2-step GMM estimation)
            --time-dummies (add time dummy variables)
            --asymptotic (uncorrected asymptotic standard errors)
Examples:   arbond 2 ; y Dx1 Dx2
            arbond 2 5 ; y Dx1 Dx2 ; Dx1
            arbond 1 ; y Dx1 Dx2 ; Dx1 GMM(x2,2,3)
            See also arbond91.inp

Carries out estimation of dynamic panel data models (that is, panel models
including one or more lags of the dependent variable) using the GMM-DIF
method set out by Arellano and Bond (1991). Please see "dpanel" for an
updated and more flexible version of this command which handles GMM-SYS as
well as GMM-DIF.

The parameter p represents the order of the autoregression for the dependent
variable. The optional parameter q indicates the maximum lag of the level of
the dependent variable to be used as an instrument. If this argument is
omitted, or given as 0, all available lags are used.

The dependent variable should be given in levels form; it will be
automatically differenced (since this estimator uses differencing to cancel
out the individual effects). The independent variables are not automatically
differenced; if you want to use differences (which will generally be the
case for ordinary quantitative variables, though perhaps not for, say, time
dummy variables) you should create the differences first then specify these
as the regressors.

The last (optional) field in the command is for specifying instruments. If
no instruments are given, it is assumed that all the independent variables
are strictly exogenous. If you specify any instruments, you should include
in the list any strictly exogenous independent variables. For predetermined
regressors, you can use the GMM function to include a specified range of
lags in block-diagonal fashion. This is illustrated in the third example
above. The first argument to GMM is the name of the variable in question,
the second is the minimum lag to be used as an instrument, and the third is
the maximum lag. If the third argument is given as 0, all available lags are
used.

By default the results of 1-step estimation are reported (with robust
standard errors). You may select 2-step estimation as an option. In both
cases tests for autocorrelation of orders 1 and 2 are provided, as well as
the Sargan overidentification test and a Wald test for the joint
significance of the regressors. Note that in this differenced model
first-order autocorrelation is not a threat to the validity of the model,
but second-order autocorrelation violates the maintained statistical
assumptions.

In the case of 2-step estimation, standard errors are by default computed
using the finite-sample correction suggested by Windmeijer (2005). The
standard asymptotic standard errors associated with the 2-step estimator are
generally reckoned to be an unreliable guide to inference, but if for some
reason you want to see them you can use the --asymptotic option to turn off
the Windmeijer correction.

If the --time-dummies option is given, a set of time dummy variables is
added to the specified regressors. The number of dummies is one less than
the maximum number of periods used in estimation, to avoid perfect
collinearity with the constant. The dummies are entered in levels; if you
wish to use time dummies in first-differenced form, you will have to define
and add these variables manually.

# arch Estimation

Arguments:  order depvar indepvars 
Example:    arch 4 y 0 x1 x2 x3

This command is retained at present for backward compatibility, but you are
better off using the maximum likelihood estimator offered by the "garch"
command; for a plain ARCH model, set the first GARCH parameter to 0.

Estimates the given model specification allowing for ARCH (Autoregressive
Conditional Heteroskedasticity). The model is first estimated via OLS, then
an auxiliary regression is run, in which the squared residual from the first
stage is regressed on its own lagged values. The final step is weighted
least squares estimation, using as weights the reciprocals of the fitted
error variances from the auxiliary regression. (If the predicted variance of
any observation in the auxiliary regression is not positive, then the
corresponding squared residual is used instead).

The alpha values displayed below the coefficients are the estimated
parameters of the ARCH process from the auxiliary regression.

See also "garch" and "modtest" (the --arch option).

Menu path:    /Model/Time series/ARCH

# arima Estimation

Arguments:  p d q [ ; P D Q ] depvar ; [ indepvars ] 
Options:    --verbose (print details of iterations)
            --vcv (print covariance matrix)
            --hessian (see below)
            --opg (see below)
            --nc (do not include a constant)
            --conditional (use conditional maximum likelihood)
            --x-12-arima (use X-12-ARIMA for estimation)
            --lbfgs (use L-BFGS-B maximizer)
            --y-diff-only (ARIMAX special, see below)
            --save-ehat (see below)
Examples:   arima 1 0 2 ; y
            arima 2 0 2 ; y 0 x1 x2 --verbose
            arima 0 1 1 ; 0 1 1 ; y --nc

If no indepvars list is given, estimates a univariate ARIMA (Autoregressive,
Integrated, Moving Average) model. The values p, d and q represent the
autoregressive (AR) order, the differencing order, and the moving average
(MA) order respectively. These values may be given in numerical form, or as
the names of pre-existing scalar variables. A d value of 1, for instance,
means that the first difference of the dependent variable should be taken
before estimating the ARMA parameters.

If you wish to include only specific AR or MA lags in the model (as opposed
to all lags up to a given order) you can substitute for p and/or q either
(a) the name of a pre-defined matrix containing a set of integer values or
(b) an expression such as {1,4}; that is, a set of lags separated by commas
and enclosed in braces.

The optional integer values P, D and Q represent the seasonal AR, order for
seasonal differencing and seasonal MA order respectively. These are
applicable only if the data have a frequency greater than 1 (for example,
quarterly or monthly data). These orders must be given in numerical form or
as scalar variables.

In the univariate case the default is to include an intercept in the model
but this can be suppressed with the --nc flag. If indepvars are added, the
model becomes ARMAX; in this case the constant should be included explicitly
if you want an intercept (as in the second example above).

An alternative form of syntax is available for this command: if you do not
want to apply differencing (either seasonal or non-seasonal), you may omit
the d and D fields altogether, rather than explicitly entering 0. In
addition, arma is a synonym or alias for arima. Thus for example the
following command is a valid way to specify an ARMA(2, 1) model:

	arma 2 1 ; y

The default is to use the "native" gretl ARMA functionality, with estimation
by exact ML using the Kalman filter; estimation via conditional ML is
available as an option. (If X-12-ARIMA is installed you have the option of
using it instead of native code.) For details regarding these options,
please see the Gretl User's Guide.

When the native exact ML code is used, estimated standard errors are by
default based on a numerical approximation to the (negative inverse of) the
Hessian, with a fallback to the outer product of the gradient (OPG) if
calculation of the numerical Hessian should fail. Two (mutually exclusive)
option flags can be used to force the issue: the --opg option forces use of
the OPG method, with no attempt to compute the Hessian, while the --hessian
flag disables the fallback to OPG. Note that failure of the numerical
Hessian computation is generally an indicator of a misspecified model.

The option --lbfgs is specific to estimation using native ARMA code and
exact ML: it calls for use of the "limited memory" L-BFGS-B algorithm in
place of the regular BFGS maximizer. This may help in some instances where
convergence is difficult to achieve.

The option --y-diff-only is specific to estimation of ARIMAX models (models
with a non-zero order of integration and including exogenous regressors),
and applies only when gretl's native exact ML is used. For such models the
default behavior is to difference both the dependent variable and the
regressors, but when this option is specified only the dependent variable is
differenced, the regressors remaining in level form.

The option --save-ehat is applicable only when using native exact ML
estimation. The effect is to make available a vector holding the optimal
estimate as of period t of the t-dated disturbance or innovation: this can
be retrieved via the accessor $ehat. These values differ from the residual
series ($uhat), which holds the one-step ahead forecast errors.

The AIC value given in connection with ARIMA models is calculated according
to the definition used in X-12-ARIMA, namely

  AIC = -2L + 2k

where L is the log-likelihood and k is the total number of parameters
estimated. Note that X-12-ARIMA does not produce information criteria such
as AIC when estimation is by conditional ML.

The AR and MA roots shown in connection with ARMA estimation are based on
the following representation of an ARMA(p, q) process:

	(1 - a_1*L - a_2*L^2 - ... - a_p*L^p)Y =
          c + (1 + b_1*L + b_2*L^2 + ... + b_q*L^q) e_t

The AR roots are therefore the solutions to

         1 - a_1*z - a_2*z^2 - ... - a_p*L^p = 0

and stability requires that these roots lie outside the unit circle.

The "frequency" figure printed in connection with AR and MA roots is the
lambda value that solves z = r * exp(i*2*pi*lambda) where z is the root in
question and r is its modulus.

Menu path:    /Model/Time series/ARIMA
Other access: Main window pop-up menu (single selection)

# biprobit Estimation

Arguments:  depvar1 depvar2 indepvars1 [ ; indepvars2 ] 
Options:    --vcv (print covariance matrix)
            --robust (robust standard errors)
            --cluster=clustvar (see "logit" for explanation)
            --opg (see below)
            --save-xbeta (see below)
            --verbose (print extra information)
Examples:   biprobit y1 y2 0 x1 x2
            biprobit y1 y2 0 x11 x12 ; 0 x21 x22
            See also biprobit.inp

Estimates a bivariate probit model, using the Newton-Raphson method to
maximize the likelihood.

The argument list starts with the two (binary) dependent variables, followed
by a list of regressors. If a second list is given, separated by a
semicolon, this is interpreted as a set of regressors specific to the second
equation, with indepvars1 being specific to the first equation; otherwise
indepvars1 is taken to represent a common set of regressors.

By default, standard errors are computed using a numerical approximation to
the Hessian at convergence. But if the --opg option is given the covariance
matrix is based on the Outer Product of the Gradient (OPG), or if the
--robust option is given QML standard errors are calculated, using a
"sandwich" of the inverse of the Hessian and the OPG.

After successful estimation, the accessor $uhat retrieves a matrix with two
columns holding the generalized residuals for the two equations; that is,
the expected values of the disturbances conditional on the observed outcomes
and covariates. By default $yhat retrieves a matrix with four columns,
holding the estimated probabilities of the four possible joint outcomes for
(y_1, y_2), in the order (1,1), (1,0), (0,1), (0,0). Alternatively, if the
option --save-xbeta is given, $yhat has two columns and holds the values of
the index functions for the respective equations.

The output includes a likelihood ratio test of the null hypothesis that the
disturbances in the two equations are uncorrelated.

# boxplot Graphs

Argument:   varlist 
Options:    --notches (show 90 percent interval for median)
            --factorized (see below)
            --panel (see below)
            --matrix=name (plot columns of named matrix)
            --output=filename (send output to specified file)

These plots display the distribution of a variable. The central box encloses
the middle 50 percent of the data, i.e. it is bounded by the first and third
quartiles. The "whiskers" extend to the minimum and maximum values. A line
is drawn across the box at the median. A "+" sign is used to indicate the
mean. If the option of showing a confidence interval for the median is
selected, this is computed via the bootstrap method and shown in the form of
dashed horizontal lines above and/or below the median.

The --factorized option allows you to examine the distribution of a chosen
variable conditional on the value of some discrete factor. For example, if a
data set contains wages and a gender dummy variable you can select the wage
variable as the target and gender as the factor, to see side-by-side
boxplots of male and female wages, as in

	boxplot wage gender --factorized

Note that in this case you must specify exactly two variables, with the
factor given second.

If the current data set is a panel, and just one variable is specified, the
--panel option produces a series of side-by-side boxplots, one for each
panel "unit" or group.

Generally, the argument varlist is required, and refers to one or more
series in the current dataset (given either by name or ID number). But if a
named matrix is supplied via the --matrix option this argument becomes
optional: by default a plot is drawn for each column of the specified
matrix.

Gretl's boxplots are generated using gnuplot, and it is possible to specify
the plot more fully by appending additional gnuplot commands, enclosed in
braces. For details, please see the help for the "gnuplot" command.

In interactive mode the result is displayed immediately. In batch mode the
default behavior is that a gnuplot command file is written in the user's
working directory, with a name on the pattern gpttmpN.plt, starting with N =
01. The actual plots may be generated later using gnuplot (under MS Windows,
wgnuplot). This behavior can be modified by use of the --output=filename
option. For details, please see the "gnuplot" command.

Menu path:    /View/Graph specified vars/Boxplots

# break Programming

Break out of a loop. This command can be used only within a loop; it causes
command execution to break out of the current (innermost) loop. See also
"loop".

# catch Programming

Syntax:     catch command

This is not a command in its own right but can be used as a prefix to most
regular commands: the effect is to prevent termination of a script if an
error occurs in executing the command. If an error does occur, this is
registered in an internal error code which can be accessed as $error (a zero
value indicates success). The value of $error should always be checked
immediately after using catch, and appropriate action taken if the command
failed.

The catch keyword cannot be used before if, elif or endif.

# chow Tests

Variants:   chow obs
            chow dummyvar --dummy
Options:    --dummy (use a pre-existing dummy variable)
            --quiet (don't print estimates for augmented model)
Examples:   chow 25
            chow 1988:1
            chow female --dummy

Must follow an OLS regression. If an observation number or date is given,
provides a test for the null hypothesis of no structural break at the given
split point. The procedure is to create a dummy variable which equals 1 from
the split point specified by obs to the end of the sample, 0 otherwise, and
also interaction terms between this dummy and the original regressors. If a
dummy variable is given, tests the null hypothesis of structural homogeneity
with respect to that dummy. Again, interaction terms are added. In either
case an augmented regression is run including the additional terms.

By default an F statistic is calculated, taking the augmented regression as
the unrestricted model and the original as the restricted. But if the
original model used a robust estimator for the covariance matrix, the test
statistic is a Wald chi-square value based on a robust estimator of the
covariance matrix for the augmented regression.

Menu path:    Model window, /Tests/Chow test

# clear Programming

Option:     --dataset (clear dataset only)

With no options, clears all saved objects, including the current dataset if
any, out of memory. Note that opening a new dataset, or using the "nulldata"
command to create an empty dataset, also has this effect, so use of "clear"
is not usually necessary.

If the --dataset option is given, then only the dataset is cleared (plus any
named lists of series); other saved objects such as named matrices and
scalars are preserved.

# coeffsum Tests

Argument:   varlist 
Example:    coeffsum xt xt_1 xr_2
            See also restrict.inp

Must follow a regression. Calculates the sum of the coefficients on the
variables in varlist. Prints this sum along with its standard error and the
p-value for the null hypothesis that the sum is zero.

Note the difference between this and "omit", which tests the null hypothesis
that the coefficients on a specified subset of independent variables are all
equal to zero.

Menu path:    Model window, /Tests/Sum of coefficients

# coint Tests

Arguments:  order depvar indepvars 
Options:    --nc (do not include a constant)
            --ct (include constant and trend)
            --ctt (include constant and quadratic trend)
            --skip-df (no DF tests on individual variables)
            --test-down (automatic lag order)
            --verbose (print extra details of regressions)
Examples:   coint 4 y x1 x2
            coint 0 y x1 x2 --ct --skip-df

The Engle-Granger (1987) cointegration test. The default procedure is: (1)
carry out Dickey-Fuller tests on the null hypothesis that each of the
variables listed has a unit root; (2) estimate the cointegrating regression;
and (3) run a DF test on the residuals from the cointegrating regression. If
the --skip-df flag is given, step (1) is omitted.

If the specified lag order is positive all the Dickey-Fuller tests use that
order, with this qualification: if the --test-down option is given, the
given value is taken as the maximum and the actual lag order used in each
case is obtained by testing down. See the "adf" command for details of this
procedure.

By default, the cointegrating regression contains a constant. If you wish to
suppress the constant, add the --nc flag. If you wish to augment the list of
deterministic terms in the cointegrating regression with a linear or
quadratic trend, add the --ct or --ctt flag. These option flags are mutually
exclusive.

P-values for this test are based on MacKinnon (1996). The relevant code is
included by kind permission of the author.

Menu path:    /Model/Time series/Cointegration test/Engle-Granger

# coint2 Tests

Arguments:  order ylist [ ; xlist ] [ ; rxlist ] 
Options:    --nc (no constant)
            --rc (restricted constant)
            --uc (unrestricted constant)
            --crt (constant and restricted trend)
            --ct (constant and unrestricted trend)
            --seasonals (include centered seasonal dummies)
            --asy (record asymptotic p-values)
            --quiet (print just the tests)
            --silent (don't print anything)
            --verbose (print details of auxiliary regressions)
Examples:   coint2 2 y x
            coint2 4 y x1 x2 --verbose
            coint2 3 y x1 x2 --rc

Carries out the Johansen test for cointegration among the variables in ylist
for the given lag order. For details of this test see the Gretl User's Guide
or Hamilton (1994), Chapter 20. P-values are computed via Doornik's gamma
approximation (Doornik, 1998). Two sets of p-values are shown for the trace
test, straight asymptotic values and values adjusted for the sample size. By
default the $pvalue accessor gets the adjusted variant, but the --asy flag
may be used to record the asymptotic values instead.

The inclusion of deterministic terms in the model is controlled by the
option flags. The default if no option is specified is to include an
"unrestricted constant", which allows for the presence of a non-zero
intercept in the cointegrating relations as well as a trend in the levels of
the endogenous variables. In the literature stemming from the work of
Johansen (see for example his 1995 book) this is often referred to as "case
3". The first four options given above, which are mutually exclusive,
produce cases 1, 2, 4 and 5 respectively. The meaning of these cases and the
criteria for selecting a case are explained in the Gretl User's Guide.

The optional lists xlist and rxlist allow you to control for specified
exogenous variables: these enter the system either unrestrictedly (xlist) or
restricted to the cointegration space (rxlist). These lists are separated
from ylist and from each other by semicolons.

The --seasonals option, which may be combined with any of the other options,
specifies the inclusion of a set of centered seasonal dummy variables. This
option is available only for quarterly or monthly data.

The following table is offered as a guide to the interpretation of the
results shown for the test, for the 3-variable case. H0 denotes the null
hypothesis, H1 the alternative hypothesis, and c the number of cointegrating
relations.

         Rank     Trace test         Lmax test
                  H0     H1          H0     H1
         ---------------------------------------
          0      c = 0  c = 3       c = 0  c = 1
          1      c = 1  c = 3       c = 1  c = 2
          2      c = 2  c = 3       c = 2  c = 3
         ---------------------------------------

See also the "vecm" command.

Menu path:    /Model/Time series/Cointegration test/Johansen

# corr Statistics

Argument:   [ varlist ] 
Options:    --uniform (ensure uniform sample)
            --spearman (Spearman's rho)
            --kendall (Kendall's tau)
            --verbose (print rankings)
Examples:   corr y x1 x2 x3
            corr ylist --uniform
            corr x y --spearman

By default, prints the pairwise correlation coefficients (Pearson's
product-moment correlation) for the variables in varlist, or for all
variables in the data set if varlist is not given. The standard behavior is
to use all available observations for computing each pairwise coefficient,
but if the --uniform option is given the sample is limited (if necessary) so
that the same set of observations is used for all the coefficients. This
option has an effect only if there are differing numbers of missing values
for the variables used.

The (mutually exclusive) options --spearman and --kendall produce,
respectively, Spearman's rank correlation rho and Kendall's rank correlation
tau in place of the default Pearson coefficient. When either of these
options is given, varlist should contain just two variables.

When a rank correlation is computed, the --verbose option can be used to
print the original and ranked data (otherwise this option is ignored).

Menu path:    /View/Correlation matrix
Other access: Main window pop-up menu (multiple selection)

# corrgm Statistics

Arguments:  series [ order ] 
Option:     --plot=mode-or-filename (see below)
Example:    corrgm x 12

Prints the values of the autocorrelation function for series, which may be
specified by name or number. The values are defined as rho(u_t, u_t-s) where
u_t is the t^th observation of the variable u and s denotes the number of
lags.

The partial autocorrelations (calculated using the Durbin-Levinson
algorithm) are also shown: these are net of the effects of intervening lags.
In addition the Ljung-Box Q statistic is printed. This may be used to test
the null hypothesis that the series is "white noise"; it is asymptotically
distributed as chi-square with degrees of freedom equal to the number of
lags used.

If an order value is specified the length of the correlogram is limited to
at most that number of lags, otherwise the length is determined
automatically, as a function of the frequency of the data and the number of
observations.

By default, a plot of the correlogram is produced: a gnuplot graph in
interactive mode or an ASCII graphic in batch mode. This can be adjusted via
the --plot option. The acceptable parameters to this option are none (to
suppress the plot); ascii (to produce a text graphic even when in
interactive mode); display (to produce a gnuplot graph even when in batch
mode); or a file name. The effect of providing a file name is as described
for the --output option of the "gnuplot" command.

Upon successful completion, the accessors $test and $pvalue contain the
corresponding figures of the Ljung-Box test for the maximum order displayed.
Note that if you just want to compute the Q statistic, you'll probably want
to use the "ljungbox" function instead.

Menu path:    /Variable/Correlogram
Other access: Main window pop-up menu (single selection)

# cusum Tests

Options:    --squares (perform the CUSUMSQ test)
            --quiet (just print the Harvey-Collier test)

Must follow the estimation of a model via OLS. Performs the CUSUM test -- or
if the --squares option is given, the CUSUMSQ test -- for parameter
stability. A series of one-step ahead forecast errors is obtained by running
a series of regressions: the first regression uses the first k observations
and is used to generate a prediction of the dependent variable at
observation k + 1; the second uses the first k + 1 observations and
generates a prediction for observation k + 2, and so on (where k is the
number of parameters in the original model).

The cumulated sum of the scaled forecast errors, or the squares of these
errors, is printed and graphed. The null hypothesis of parameter stability
is rejected at the 5 percent significance level if the cumulated sum strays
outside of the 95 percent confidence band.

In the case of the CUSUM test, the Harvey-Collier t-statistic for testing
the null hypothesis of parameter stability is also printed. See Greene's
Econometric Analysis for details. For the CUSUMSQ test, the 95 percent
confidence band is calculated using the algorithm given in Edgerton and
Wells (1994).

Menu path:    Model window, /Tests/CUSUM(SQ)

# data Dataset

Argument:   varlist 
Option:     --quiet (don't report results except on error)

Reads the variables in varlist from a database (gretl, RATS 4.0 or PcGive),
which must have been opened previously using the "open" command. The data
frequency and sample range may be established via the "setobs" and "smpl"
commands prior to using this command. Here is a full example:

	open macrodat.rat
	setobs 4 1959:1
	smpl ; 1999:4
	data GDP_JP GDP_UK

The commands above open a database named macrodat.rat, establish a quarterly
data set starting in the first quarter of 1959 and ending in the fourth
quarter of 1999, and then import the series named GDP_JP and GDP_UK.

If setobs and smpl are not specified in this way, the data frequency and
sample range are set using the first variable read from the database.

If the series to be read are of higher frequency than the working data set,
you may specify a compaction method as below:

	data (compact=average) LHUR PUNEW

The four available compaction methods are "average" (takes the mean of the
high frequency observations), "last" (uses the last observation), "first"
and "sum". If no method is specified, the default is to use the average.

Menu path:    /File/Databases

# dataset Dataset

Arguments:  keyword parameters 
Examples:   dataset addobs 24
            dataset insobs 10
            dataset compact 1
            dataset compact 4 last
            dataset expand interp
            dataset transpose
            dataset sortby x1
            dataset resample 500
            dataset renumber x 4
            dataset clear

Performs various operations on the data set as a whole, depending on the
given keyword, which must be addobs, insobs, clear, compact, expand,
transpose, sortby, dsortby, resample or renumber. Note: with the exception
of clear, these actions are not available when the dataset is currently
subsampled by selection of cases on some Boolean criterion.

addobs: Must be followed by a positive integer. Adds the specified number of
extra observations to the end of the working dataset. This is primarily
intended for forecasting purposes. The values of most variables over the
additional range will be set to missing, but certain deterministic variables
are recognized and extended, namely, a simple linear trend and periodic
dummy variables.

insobs: Must be followed by a positive integer no greater than the current
number of observations. Inserts a single observation at the specified
position. All subsequent data are shifted by one place and the dataset is
extended by one observation. All variables apart from the constant are given
missing values at the new observation. This action is not available for
panel datasets.

clear: No parameter required. Clears out the current data, returning gretl
to its initial "empty" state.

compact: Must be followed by a positive integer representing a new data
frequency, which should be lower than the current frequency (for example, a
value of 4 when the current frequency is 12 indicates compaction from
monthly to quarterly). This command is available for time series data only;
it compacts all the series in the data set to the new frequency. A second
parameter may be given, namely one of sum, first or last, to specify,
respectively, compaction using the sum of the higher-frequency values,
start-of-period values or end-of-period values. The default is to compact by
averaging.

expand: This command is only available for annual or quarterly time series
data: annual data can be expanded to quarterly, and quarterly data to
monthly frequency. By default all the series in the data set are padded out
to the new frequency by repeating the existing values, but if the modifier
interp is appended then the series are expanded using Chow-Lin interpolation
(see Chow and Lin, 1971): the regressors are a constant and quadratic trend
and an AR(1) disturbance process is assumed.

transpose: No additional parameter required. Transposes the current data
set. That is, each observation (row) in the current data set will be treated
as a variable (column), and each variable as an observation. This command
may be useful if data have been read from some external source in which the
rows of the data table represent variables.

sortby: The name of a single series or list is required. If one series is
given, the observations on all variables in the dataset are re-ordered by
increasing value of the specified series. If a list is given, the sort
proceeds hierarchically: if the observations are tied in sort order with
respect to the first key variable then the second key is used to break the
tie, and so on until the tie is broken or the keys are exhausted. Note that
this command is available only for undated data.

dsortby: Works as sortby except that the re-ordering is by decreasing value
of the key series.

resample: Constructs a new dataset by random sampling, with replacement, of
the rows of the current dataset. One argument is required, namely the number
of rows to include. This may be less than, equal to, or greater than the
number of observations in the original data. The original dataset can be
retrieved via the command smpl full.

renumber: Requires the name of an existing series followed by an integer
between 1 and the number of series in the dataset minus one. Moves the
specified series to the specified position in the dataset, renumbering the
other series accordingly. (Position 0 is occupied by the constant, which
cannot be moved.)

Menu path:    /Data

# debug Programming

Argument:   function 

Experimental debugger for user-defined functions, available in the
command-line program, gretlcli, and in the GUI console. The debug command
should be invoked after the function in question is defined but before it is
called. The effect is that execution pauses when the function is called and
a special prompt is shown.

At the debugging prompt you can type next to execute the next command in the
function, or continue to allow execution of the function to continue
unimpeded. These commands can be abbreviated as n and c respectively. You
can also interpolate an instruction at this prompt, for example a print
command to reveal the current value of some variable of interest.

# delete Dataset

Argument:   varname 
Options:    --db (delete series from database)
            --type=type-name (all variables of given type)

This command is an all-purpose destructor for named variables (whether
series, scalars, matrices, strings or bundles). It should be used with
caution; no confirmation is asked.

In the case of series, varname may take the form of a named list, in which
case all series in the list are deleted, or it may take the form of an
explicit list of series given by name or ID number. Note that when you
delete series any series with higher ID numbers than those on the deletion
list will be re-numbered.

If the --db option is given, this command deletes the listed series not from
the current dataset but from a gretl database, assuming that a database has
been opened, and the user has write permission for file in question. See
also the "open" command.

If the --type option is given it must be accompanied by one of the following
type-names: matrix, bundle, string, list, or scalar. The effect is to delete
all variables of the given type. In this case (only), no varname argument
should be given.

Menu path:    Main window pop-up (single selection)

# diff Transformations

Argument:   varlist 

The first difference of each variable in varlist is obtained and the result
stored in a new variable with the prefix d_. Thus "diff x y" creates the new
variables

	d_x = x(t) - x(t-1)
	d_y = y(t) - y(t-1)

Menu path:    /Add/First differences of selected variables

# difftest Tests

Arguments:  series1 series2 
Options:    --sign (Sign test, the default)
            --rank-sum (Wilcoxon rank-sum test)
            --signed-rank (Wilcoxon signed-rank test)
            --verbose (print extra output)

Carries out a nonparametric test for a difference between two populations or
groups, the specific test depending on the option selected.

With the --sign option, the Sign test is performed. This test is based on
the fact that if two samples, x and y, are drawn randomly from the same
distribution, the probability that x_i > y_i, for each observation i, should
equal 0.5. The test statistic is w, the number of observations for which x_i
> y_i. Under the null hypothesis this follows the Binomial distribution with
parameters (n, 0.5), where n is the number of observations.

With the --rank-sum option, the Wilcoxon rank-sum test is performed. This
test proceeds by ranking the observations from both samples jointly, from
smallest to largest, then finding the sum of the ranks of the observations
from one of the samples. The two samples do not have to be of the same size,
and if they differ the smaller sample is used in calculating the rank-sum.
Under the null hypothesis that the samples are drawn from populations with
the same median, the probability distribution of the rank-sum can be
computed for any given sample sizes; and for reasonably large samples a
close Normal approximation exists.

With the --signed-rank option, the Wilcoxon signed-rank test is performed.
This is designed for matched data pairs such as, for example, the values of
a variable for a sample of individuals before and after some treatment. The
test proceeds by finding the differences between the paired observations,
x_i - y_i, ranking these differences by absolute value, then assigning to
each pair a signed rank, the sign agreeing with the sign of the difference.
One then calculates W_+, the sum of the positive signed ranks. As with the
rank-sum test, this statistic has a well-defined distribution under the null
that the median difference is zero, which converges to the Normal for
samples of reasonable size.

For the Wilcoxon tests, if the --verbose option is given then the ranking is
printed. (This option has no effect if the Sign test is selected.)

# discrete Transformations

Argument:   varlist 
Option:     --reverse (mark variables as continuous)

Marks each variable in varlist as being discrete. By default all variables
are treated as continuous; marking a variable as discrete affects the way
the variable is handled in frequency plots, and also allows you to select
the variable for the command "dummify".

If the --reverse flag is given, the operation is reversed; that is, the
variables in varlist are marked as being continuous.

Menu path:    /Variable/Edit attributes

# dpanel Estimation

Argument:   p ; depvar indepvars [ ; instruments ] 
Options:    --quiet (don't show estimated model)
            --vcv (print covariance matrix)
            --two-step (perform 2-step GMM estimation)
            --system (add equations in levels)
            --time-dummies (add time dummy variables)
            --dpdstyle (emulate DPD package for Ox)
            --asymptotic (uncorrected asymptotic standard errors)
Examples:   dpanel 2 ; y x1 x2
            dpanel 2 ; y x1 x2 --system
            dpanel {2 3} ; y x1 x2 ; x1
            dpanel 1 ; y x1 x2 ; x1 GMM(x2,2,3)
            See also bbond98.inp

Carries out estimation of dynamic panel data models (that is, panel models
including one or more lags of the dependent variable) using either the
GMM-DIF or GMM-SYS method.

The parameter p represents the order of the autoregression for the dependent
variable. In the simplest case this is a scalar value, but a pre-defined
matrix may be given for this argument, to specify a set of (possibly
non-contiguous) lags to be used.

The dependent variable and regressors should be given in levels form; they
will be differenced automatically (since this estimator uses differencing to
cancel out the individual effects).

The last (optional) field in the command is for specifying instruments. If
no instruments are given, it is assumed that all the independent variables
are strictly exogenous. If you specify any instruments, you should include
in the list any strictly exogenous independent variables. For predetermined
regressors, you can use the GMM function to include a specified range of
lags in block-diagonal fashion. This is illustrated in the third example
above. The first argument to GMM is the name of the variable in question,
the second is the minimum lag to be used as an instrument, and the third is
the maximum lag. The same syntax can be used with the GMMlevel function to
specify GMM-type instruments for the equations in levels.

By default the results of 1-step estimation are reported (with robust
standard errors). You may select 2-step estimation as an option. In both
cases tests for autocorrelation of orders 1 and 2 are provided, as well as
the Sargan overidentification test and a Wald test for the joint
significance of the regressors. Note that in this differenced model
first-order autocorrelation is not a threat to the validity of the model,
but second-order autocorrelation violates the maintained statistical
assumptions.

In the case of 2-step estimation, standard errors are by default computed
using the finite-sample correction suggested by Windmeijer (2005). The
standard asymptotic standard errors associated with the 2-step estimator are
generally reckoned to be an unreliable guide to inference, but if for some
reason you want to see them you can use the --asymptotic option to turn off
the Windmeijer correction.

If the --time-dummies option is given, a set of time dummy variables is
added to the specified regressors. The number of dummies is one less than
the maximum number of periods used in estimation, to avoid perfect
collinearity with the constant. The dummies are entered in differenced form
unless the --dpdstyle option is given, in which case they are entered in
levels.

For further details and examples, please see the Gretl User's Guide.

Menu path:    /Model/Panel/Dynamic panel model

# dummify Transformations

Argument:   varlist 
Options:    --drop-first (omit lowest value from encoding)
            --drop-last (omit highest value from encoding)

For any suitable variables in varlist, creates a set of dummy variables
coding for the distinct values of that variable. Suitable variables are
those that have been explicitly marked as discrete, or those that take on a
fairly small number of values all of which are "fairly round" (multiples of
0.25).

By default a dummy variable is added for each distinct value of the variable
in question. For example if a discrete variable x has 5 distinct values, 5
dummy variables will be added to the data set, with names Dx_1, Dx_2 and so
on. The first dummy variable will have value 1 for observations where x
takes on its smallest value, 0 otherwise; the next dummy will have value 1
when x takes on its second-smallest value, and so on. If one of the option
flags --drop-first or --drop-last is added, then either the lowest or the
highest value of each variable is omitted from the encoding (which may be
useful for avoiding the "dummy variable trap").

This command can also be embedded in the context of a regression
specification. For example, the following line specifies a model where y is
regressed on the set of dummy variables coding for x. (Option flags cannot
be passed to "dummify" in this context.)

	ols y dummify(x)

# duration Estimation

Arguments:  depvar indepvars [ ; censvar ] 
Options:    --exponential (use exponential distribution)
            --loglogistic (use log-logistic distribution)
            --lognormal (use log-normal distribution)
            --medians (fitted values are medians)
            --robust (robust (QML) standard errors)
            --cluster=clustvar (see "logit" for explanation)
            --vcv (print covariance matrix)
            --verbose (print details of iterations)
Examples:   duration y 0 x1 x2
            duration y 0 x1 x2 ; cens

Estimates a duration model: the dependent variable (which must be positive)
represents the duration of some state of affairs, for example the length of
spells of unemployment for a cross-section of respondents. By default the
Weibull distribution is used but the exponential, log-logistic and
log-normal distributions are also available.

If some of the duration measurements are right-censored (e.g. an
individual's spell of unemployment has not come to an end within the period
of observation) then you should supply the trailing argument censvar, a
series in which non-zero values indicate right-censored cases.

By default the fitted values obtained via the accessor $yhat are the
conditional means of the durations, but if the --medians option is given
then $yhat provides the conditional medians instead.

Please see the Gretl User's Guide for details.

Menu path:    /Model/Limited dependent variable/Duration data...

# elif Programming

See "if".

# else Programming

See "if". Note that "else" requires a line to itself, before the following
conditional command. You can append a comment, as in

	else # OK, do something different

But you cannot append a command, as in

	else x = 5 # wrong!

# end Programming

Ends a block of commands of some sort. For example, "end system" terminates
an equation "system".

# endif Programming

See "if".

# endloop Programming

Marks the end of a command loop. See "loop".

# eqnprint Printing

Argument:   [ -f filename ] 
Option:     --complete (Create a complete document)

Must follow the estimation of a model. Prints the estimated model in the
form of a LaTeX equation. If a filename is specified using the -f flag
output goes to that file, otherwise it goes to a file with a name of the
form equation_N.tex, where N is the number of models estimated to date in
the current session. See also "tabprint".

If the --complete flag is given, the LaTeX file is a complete document,
ready for processing; otherwise it must be included in a document.

Menu path:    Model window, /LaTeX

# equation Estimation

Arguments:  depvar indepvars 
Example:    equation y x1 x2 x3 const

Specifies an equation within a system of equations (see "system"). The
syntax for specifying an equation within an SUR system is the same as that
for, e.g., "ols". For an equation within a Three-Stage Least Squares system
you may either (a) give an OLS-type equation specification and provide a
common list of instruments using the "instr" keyword (again, see "system"),
or (b) use the same equation syntax as for "tsls".

# estimate Estimation

Arguments:  [ systemname ] [ estimator ] 
Options:    --iterate (iterate to convergence)
            --no-df-corr (no degrees of freedom correction)
            --geomean (see below)
            --quiet (don't print results)
            --verbose (print details of iterations)
Examples:   estimate "Klein Model 1" method=fiml
            estimate Sys1 method=sur
            estimate Sys1 method=sur --iterate

Calls for estimation of a system of equations, which must have been
previously defined using the "system" command. The name of the system should
be given first, surrounded by double quotes if the name contains spaces. The
estimator, which must be one of "ols", "tsls", "sur", "3sls", "fiml" or
"liml", is preceded by the string method=. These arguments are optional if
the system in question has already been estimated and occupies the place of
the "last model"; in that case the estimator defaults to the previously used
value.

If the system in question has had a set of restrictions applied (see the
"restrict" command), estimation will be subject to the specified
restrictions.

If the estimation method is "sur" or "3sls" and the --iterate flag is given,
the estimator will be iterated. In the case of SUR, if the procedure
converges the results are maximum likelihood estimates. Iteration of
three-stage least squares, however, does not in general converge on the
full-information maximum likelihood results. The --iterate flag is ignored
for other methods of estimation.

If the equation-by-equation estimators "ols" or "tsls" are chosen, the
default is to apply a degrees of freedom correction when calculating
standard errors. This can be suppressed using the --no-df-corr flag. This
flag has no effect with the other estimators; no degrees of freedom
correction is applied in any case.

By default, the formula used in calculating the elements of the
cross-equation covariance matrix is

  sigma(i,j) = u(i)' * u(j) / T

If the --geomean flag is given, a degrees of freedom correction is applied:
the formula is

  sigma(i,j) = u(i)' * u(j) / sqrt((T - ki) * (T - kj))

where the ks denote the number of independent parameters in each equation.

If the --verbose option is given and an iterative method is specified,
details of the iterations are printed.

# fcast Prediction

Arguments:  [ startobs endobs ] [ steps-ahead ] [ varname ] 
Options:    --dynamic (create dynamic forecast)
            --static (create static forecast)
            --out-of-sample (generate post-sample forecast)
            --no-stats (don't print forecast statistics)
            --quiet (don't print anything)
            --rolling (see below)
            --plot=filename (see below)
Examples:   fcast 1997:1 2001:4 f1
            fcast fit2
            fcast 2004:1 2008:3 4 rfcast --rolling

Must follow an estimation command. Forecasts are generated for a certain
range of observations: if startobs and endobs are given, for that range (if
possible); otherwise if the --out-of-sample option is given, for
observations following the range over which the model was estimated;
otherwise over the currently defined sample range. If an out-of-sample
forecast is requested but no relevant observations are available, an error
is flagged. Depending on the nature of the model, standard errors may also
be generated; see below. Also see below for the special effect of the
--rolling option.

If the last model estimated is a single equation, then the optional varname
argument has the following effect: the forecast values are not printed, but
are saved to the dataset under the given name. If the last model is a system
of equations, varname has a different effect, namely selecting a particular
endogenous variable for forecasting (the default being to produce forecasts
for all the endogenous variables). In the system case, or if varname is not
given, the forecast values can be retrieved using the accessor $fcast, and
the standard errors, if available, via $fcerr.

The choice between a static and a dynamic forecast applies only in the case
of dynamic models, with an autoregressive error process or including one or
more lagged values of the dependent variable as regressors. Static forecasts
are one step ahead, based on realized values from the previous period, while
dynamic forecasts employ the chain rule of forecasting. For example, if a
forecast for y in 2008 requires as input a value of y for 2007, a static
forecast is impossible without actual data for 2007. A dynamic forecast for
2008 is possible if a prior forecast can be substituted for y in 2007.

The default is to give a static forecast for any portion of the forecast
range that lies within the sample range over which the model was estimated,
and a dynamic forecast (if relevant) out of sample. The --dynamic option
requests a dynamic forecast from the earliest possible date, and the
--static option requests a static forecast even out of sample.

The --rolling option is presently available only for single-equation models
estimated via OLS. When this option is given the forecasts are recursive.
That is, each forecast is generated from an estimate of the given model
using data from a fixed starting point (namely, the start of the sample
range for the original estimation) up to the forecast date minus k, where k
is the number of steps ahead, which must be given in the steps-ahead
argument. The forecasts are always dynamic if this is applicable. Note that
the steps-ahead argument should be given only in conjunction with the
--rolling option.

The --plot option (available only in the case of single-equation estimation)
calls for a plot file to be produced, containing a graphical representation
of the forecast. The suffix of the filename argument to this option controls
the format of the plot: .eps for EPS, .pdf for PDF, .png for PNG, .plt for a
gnuplot command file. The dummy filename display can be used to force
display of the plot in a window. For example,

	fcast --plot=fc.pdf

will generate a graphic in PDF format. Absolute pathnames are respected,
otherwise files are written to the gretl working directory.

The nature of the forecast standard errors (if available) depends on the
nature of the model and the forecast. For static linear models standard
errors are computed using the method outlined by Davidson and MacKinnon
(2004); they incorporate both uncertainty due to the error process and
parameter uncertainty (summarized in the covariance matrix of the parameter
estimates). For dynamic models, forecast standard errors are computed only
in the case of a dynamic forecast, and they do not incorporate parameter
uncertainty. For nonlinear models, forecast standard errors are not
presently available.

Menu path:    Model window, /Analysis/Forecasts

# foreign Programming

Syntax:     foreign language=lang
Options:    --send-data (pre-load the current dataset; see below)
            --quiet (suppress output from foreign program)

This command opens a special mode in which commands to be executed by
another program are accepted. You exit this mode with end foreign; at this
point the stacked commands are executed.

At present the "foreign" programs supported in this way are GNU R
(language=R), Jurgen Doornik's Ox (language=Ox), GNU Octave
(language=Octave), Python and, to a lesser extent, Stata. Language names are
recognized on a case-insensitive basis.

In connection with R, Octave and Stata the --send-data option has the effect
of making the entire current gretl dataset available within the target
program.

See the Gretl User's Guide for details and examples.

# fractint Statistics

Arguments:  series [ order ] 
Options:    --gph (do Geweke and Porter-Hudak test)
            --all (do both tests)
            --quiet (don't print results)

Tests the specified series for fractional integration ("long memory"). The
null hypothesis is that the integration order of the series is zero. By
default the local Whittle estimator (Robinson, 1995) is used but if the
--gph option is given the GPH test (Geweke and Porter-Hudak, 1983) is
performed instead. If the --all flag is given then the results of both tests
are printed.

For details on this sort of test, see Phillips and Shimotsu (2004).

If the optional order argument is not given the order for the test(s) is set
automatically as the lesser of T/2 and T^0.6.

The results can be retrieved using the accessors $test and $pvalue. These
values are based on the Local Whittle Estimator unless the --gph option is
given.

Menu path:    /Variable/Unit root tests/Fractional integration

# freq Statistics

Argument:   var 
Options:    --nbins=n (specify number of bins)
            --min=minval (specify minimum, see below)
            --binwidth=width (specify bin width, see below)
            --quiet (suppress printing of graph)
            --normal (test for the normal distribution)
            --gamma (test for gamma distribution)
            --silent (don't print anything)
            --show-plot (see below)
            --matrix=name (use column of named matrix)
Examples:   freq x
            freq x --normal
            freq x --nbins=5
            freq x --min=0 --binwidth=0.10

With no options given, displays the frequency distribution for the series
var (given by name or number), with the number of bins and their size chosen
automatically.

If the --matrix option is given, var (which must be an integer) is instead
interpreted as a 1-based index that selects a column from the named matrix.

To control the presentation of the distribution you may specify either the
number of bins or the minimum value plus the width of the bins, as shown in
the last two examples above. The --min option sets the lower limit of the
left-most bin.

If the --normal option is given, the Doornik-Hansen chi-square test for
normality is computed. If the --gamma option is given, the test for
normality is replaced by Locke's nonparametric test for the null hypothesis
that the variable follows the gamma distribution; see Locke (1976), Shapiro
and Chen (2001). Note that the parameterization of the gamma distribution
used in gretl is (shape, scale).

In interactive mode a graph of the distribution is displayed by default. The
--quiet flag can be used to suppress this. Conversely, the graph is not
usually shown when the "freq" is used in a script, but you can force its
display by giving the --show-plot option. (This does not apply when using
the command-line program, gretlcli.)

The --silent flag suppresses the usual output entirely. This makes sense
only in conjunction with one or other of the distribution test options: the
test statistic and its p-value are recorded, and can be retrieved using the
accessors $test and $pvalue.

Menu path:    /Variable/Frequency distribution

# function Programming

Argument:   fnname 

Opens a block of statements in which a function is defined. This block must
be closed with end function. Please see the Gretl User's Guide for details.

# garch Estimation

Arguments:  p q ; depvar [ indepvars ] 
Options:    --robust (robust standard errors)
            --verbose (print details of iterations)
            --vcv (print covariance matrix)
            --nc (do not include a constant)
            --stdresid (standardize the residuals)
            --fcp (use Fiorentini, Calzolari, Panattoni algorithm)
            --arma-init (initial variance parameters from ARMA)
Examples:   garch 1 1 ; y
            garch 1 1 ; y 0 x1 x2 --robust

Estimates a GARCH model (GARCH = Generalized Autoregressive Conditional
Heteroskedasticity), either a univariate model or, if indepvars are
specified, including the given exogenous variables. The integer values p and
q (which may be given in numerical form or as the names of pre-existing
scalar variables) represent the lag orders in the conditional variance
equation:

  h(t) = a(0) + sum(i=1 to q) a(i)*u(t-i)^2 + sum(j=1 to p) b(j)*h(t-j)

The parameter p therefore represents the Generalized (or "AR") order, while
q represents the regular ARCH (or "MA") order. If p is non-zero, q must also
be non-zero otherwise the model is unidentified. However, you can estimate a
regular ARCH model by setting q to a positive value and p to zero. The sum
of p and q must be no greater than 5. Note that a constant is automatically
included in the mean equation unless the --nc option is given.

By default native gretl code is used in estimation of GARCH models, but you
also have the option of using the algorithm of Fiorentini, Calzolari and
Panattoni (1996). The former uses the BFGS maximizer while the latter uses
the information matrix to maximize the likelihood, with fine-tuning via the
Hessian.

Several variant estimators of the covariance matrix are available with this
command. By default, the Hessian is used unless the --robust option is
given, in which case the QML (White) covariance matrix is used. Other
possibilities (e.g. the information matrix, or the Bollerslev-Wooldridge
estimator) can be specified using the "set" command.

By default, the estimates of the variance parameters are initialized using
the unconditional error variance from initial OLS estimation for the
constant, and small positive values for the coefficients on the past values
of the squared error and the error variance. The flag --arma-init calls for
the starting values of these parameters to be set using an initial ARMA
model, exploiting the relationship between GARCH and ARMA set out in Chapter
21 of Hamilton's Time Series Analysis. In some cases this may improve the
chances of convergence.

The GARCH residuals and estimated conditional variance can be retrieved as
$uhat and $h respectively. For example, to get the conditional variance:

	series ht = $h

If the --stdresid option is given, the $uhat values are divided by the
square root of h_t.

Menu path:    /Model/Time series/GARCH

# genr Dataset

Arguments:  newvar = formula 

NOTE: this command has undergone numerous changes and enhancements since the
following help text was written, so for comprehensive and updated info on
this command you'll want to refer to the Gretl User's Guide. On the other
hand, this help does not contain anything actually erroneous, so take the
following as "you have this, plus more".

In the appropriate context, series, scalar, matrix, string and bundle are
synonyms for this command.

Creates new variables, often via transformations of existing variables. See
also "diff", "logs", "lags", "ldiff", "sdiff" and "square" for shortcuts. In
the context of a genr formula, existing variables must be referenced by
name, not ID number. The formula should be a well-formed combination of
variable names, constants, operators and functions (described below). Note
that further details on some aspects of this command can be found in the
Gretl User's Guide.

A genr command may yield either a series or a scalar result. For example,
the formula x2 = x * 2 naturally yields a series if the variable x is a
series and a scalar if x is a scalar. The formulae x = 0 and mx = mean(x)
naturally return scalars. Under some circumstances you may want to have a
scalar result expanded into a series or vector. You can do this by using
series as an "alias" for the genr command. For example, series x = 0
produces a series all of whose values are set to 0. You can also use scalar
as an alias for genr. It is not possible to coerce a vector result into a
scalar, but use of this keyword indicates that the result should be a
scalar: if it is not, an error occurs.

When a formula yields a series result, the range over which the result is
written to the target variable depends on the current sample setting. It is
possible, therefore, to define a series piecewise using the smpl command in
conjunction with genr.

Supported arithmetical operators are, in order of precedence: ^
(exponentiation); *, / and % (modulus or remainder); + and -.

The available Boolean operators are (again, in order of precedence): !
(negation), && (logical AND), || (logical OR), >, <, =, >= (greater than or
equal), <= (less than or equal) and != (not equal). The Boolean operators
can be used in constructing dummy variables: for instance (x > 10) returns 1
if x > 10, 0 otherwise.

Built-in constants are pi and NA. The latter is the missing value code: you
can initialize a variable to the missing value with scalar x = NA.

The genr command supports a wide range of mathematical and statistical
functions, including all the common ones plus several that are special to
econometrics. In addition it offers access to numerous internal variables
that are defined in the course of running regressions, doing hypothesis
tests, and so on. For a listing of functions and accessors, type "help
functions".

Besides the operators and functions noted above there are some special uses
of "genr":

  "genr time" creates a time trend variable (1,2,3,...) called "time". "genr
  index" does the same thing except that the variable is called index.

  "genr dummy" creates dummy variables up to the periodicity of the data. In
  the case of quarterly data (periodicity 4), the program creates dq1 = 1
  for first quarter and 0 in other quarters, dq2 = 1 for the second quarter
  and 0 in other quarters, and so on. With monthly data the dummies are
  named dm1, dm2, and so on. With other frequencies the names are dummy_1,
  dummy_2, etc.

  "genr unitdum" and "genr timedum" create sets of special dummy variables
  for use with panel data. The first codes for the cross-sectional units and
  the second for the time period of the observations.

Note: In the command-line program, "genr" commands that retrieve
model-related data always reference the model that was estimated most
recently. This is also true in the GUI program, if one uses "genr" in the
"gretl console" or enters a formula using the "Define new variable" option
under the Add menu in the main window. With the GUI, however, you have the
option of retrieving data from any model currently displayed in a window
(whether or not it's the most recent model). You do this under the "Save"
menu in the model's window.

The special variable obs serves as an index of the observations. For
instance genr dum = (obs=15) will generate a dummy variable that has value 1
for observation 15, 0 otherwise. You can also use this variable to pick out
particular observations by date or name. For example, genr d = (obs>1986:4),
genr d = (obs>"2008-04-01"), or genr d = (obs="CA"). If daily dates or
observation labels are used in this context, they should be enclosed in
double quotes. Quarterly and monthly dates (with a colon) may be used
unquoted. Note that in the case of annual time series data, the year is not
distinguishable syntactically from a plain integer; therefore if you wish to
compare observations against obs by year you must use the function obsnum to
convert the year to a 1-based index value, as in genr d =
(obs>obsnum(1986)).

Scalar values can be pulled from a series in the context of a genr formula,
using the syntax varname[obs]. The obs value can be given by number or date.
Examples: x[5], CPI[1996:01]. For daily data, the form YYYY-MM-DD should be
used, e.g. ibm[1970-01-23].

An individual observation in a series can be modified via genr. To do this,
a valid observation number or date, in square brackets, must be appended to
the name of the variable on the left-hand side of the formula. For example,
genr x[3] = 30 or genr x[1950:04] = 303.7.

  Formula                Comment
  -------                -------
  y = x1^3               x1 cubed
  y = ln((x1+x2)/x3)     
  z = x>y                z(t) = 1 if x(t) > y(t), otherwise 0
  y = x(-2)              x lagged 2 periods
  y = x(+2)              x led 2 periods
  y = diff(x)            y(t) = x(t) - x(t-1)
  y = ldiff(x)           y(t) = log x(t) - log x(t-1), the instantaneous rate 
                         of growth of x
  y = sort(x)            sorts x in increasing order and stores in y
  y = dsort(x)           sort x in decreasing order
  y = int(x)             truncate x and store its integer value as y
  y = abs(x)             store the absolute values of x
  y = sum(x)             sum x values excluding missing NA entries
  y = cum(x)             cumulation: y(t) = the sum from s=1 to s=t of x(s) 
  aa = $ess              set aa equal to the Error Sum of Squares from last 
                         regression
  x = $coeff(sqft)       grab the estimated coefficient on the variable sqft 
                         from the last regression
  rho4 = $rho(4)         grab the 4th-order autoregressive coefficient from 
                         the last model (presumes an ar model)
  cvx1x2 = $vcv(x1, x2)  grab the estimated coefficient covariance of vars x1 
                         and x2 from the last model
  foo = uniform()        uniform pseudo-random variable in range 0-1
  bar = 3 * normal()     normal pseudo-random variable, mu = 0, sigma = 3
  samp = ok(x)           = 1 for observations where x is not missing.

Menu path:    /Add/Define new variable
Other access: Main window pop-up menu

# gmm Estimation

Options:    --two-step (two step estimation)
            --iterate (iterated GMM)
            --vcv (print covariance matrix)
            --verbose (print details of iterations)
            --lbfgs (use L-BFGS-B instead of regular BFGS)

Performs Generalized Method of Moments (GMM) estimation using the BFGS
(Broyden, Fletcher, Goldfarb, Shanno) algorithm. You must specify one or
more commands for updating the relevant quantities (typically GMM
residuals), one or more sets of orthogonality conditions, an initial matrix
of weights, and a listing of the parameters to be estimated, all enclosed
between the tags gmm and end gmm. Any options should be appended to the end
gmm line.

Please see the Gretl User's Guide for details on this command. Here we just
illustrate with a simple example.

	gmm e = y - X*b
	  orthog e ; W
	  weights V
	  params b
	end gmm

In the example above we assume that y and X are data matrices, b is an
appropriately sized vector of parameter values, W is a matrix of
instruments, and V is a suitable matrix of weights. The statement

	orthog e ; W

indicates that the residual vector e is in principle orthogonal to each of
the instruments composing the columns of W.

Menu path:    /Model/GMM

# gnuplot Graphs

Arguments:  yvars xvar [ dumvar ] 
Options:    --with-lines[=varspec] (use lines, not points)
            --with-lp[=varspec] (use lines and points)
            --with-impulses[=varspec] (use vertical lines)
            --time-series (plot against time)
            --suppress-fitted (don't show fitted line)
            --single-yaxis (force use of just one y-axis)
            --linear-fit (show least squares fit)
            --inverse-fit (show inverse fit)
            --quadratic-fit (show quadratic fit)
            --cubic-fit (show cubic fit)
            --loess-fit (show loess fit)
            --semilog-fit (show semilog fit)
            --dummy (see below)
            --matrix=name (plot columns of named matrix)
            --output=filename (send output to specified file)
            --input=filename (take input from specified file)
Examples:   gnuplot y1 y2 x
            gnuplot x --time-series --with-lines
            gnuplot wages educ gender --dummy
            gnuplot y1 y2 x --with-lines=y2

The variables in the list yvars are graphed against xvar. For a time series
plot you may either give time as xvar or use the option flag --time-series.

By default, data-points are shown as points; this can be overridden by
giving one of the options --with-lines, --with-lp or --with-impulses. If
more than one variable is to be plotted on the y axis, the effect of these
options may be confined to a subset of the variables by using the varspec
parameter. This should take the form of a comma-separated listing of the
names or numbers of the variables to be plotted with lines or impulses
respectively. For instance, the final example above shows how to plot y1 and
y2 against x, such that y2 is represented by a line but y1 by points.

If the --dummy option is selected, exactly three variables should be given:
a single y variable, an x variable, and dvar, a discrete variable. The
effect is to plot yvar against xvar with the points shown in different
colors depending on the value of dvar at the given observation.

Generally, the arguments yvars and xvar are required, and refer to series in
the current dataset (given either by name or ID number). But if a named
matrix is supplied via the --matrix option these arguments become optional:
if the specified matrix has k columns, by default the first k - 1 columns
are treated as the yvars and the last column as xvar. If the --time-series
option is given, however, all k columns are plotted against time. If you
wish to plot selected columns of the matrix, you should specify yvars and
xvar in the form of 1-based column numbers. For example if you want a
scatterplot of column 2 of matrix M against column 1, you can do:

	gnuplot 2 1 --matrix=M

In interactive mode the plot is displayed immediately. In batch mode the
default behavior is that a gnuplot command file is written in the user's
working directory, with a name on the pattern gpttmpN.plt, starting with N =
01. The actual plots may be generated later using gnuplot (under MS Windows,
wgnuplot). This behavior can be modified by use of the --output=filename
option. This option controls the filename used, and at the same time allows
you to specify a particular output format via the three-letter extension of
the file name, as follows: .eps results in the production of an Encapsulated
PostScript (EPS) file; .pdf produces PDF; .png produces PNG format, .emf
calls for EMF (Enhanced MetaFile), .fig calls for an Xfig file, and .svg for
SVG (Scalable Vector Graphics). If the dummy filename "display" is given
then the plot is shown on screen as in interactive mode. If a filename with
any extension other than those just mentioned is given, a gnuplot command
file is written.

The various "fit" options are applicable only for bivariate scatterplots and
single time-series plots. The default behavior for a scatterplot is to show
the OLS fit if the slope coefficient is significant at the 10 percent level;
this can be suppressed via the --suppress-fitted option. The default
behavior for time-series is not to show a fitted line. If the --linear
option is given, the OLS line is shown regardless of whether or not it is
significant. The other fit options (--inverse, --quadratic, --cubic, --loess
and --semilog) produce respectively an inverse fit (regression of y on 1/x),
a quadratic fit, a cubic fit, a loess fit, and a semilog fit. Loess (also
sometimes called "lowess") is a robust locally weighted regression. By
semilog, we mean a regression of log y on x (or time); the fitted line
represents the conditional expectation of y, obtained by exponentiation.

A further option to this command is available: following the specification
of the variables to be plotted and the option flag (if any), you may add
literal gnuplot commands to control the appearance of the plot (for example,
setting the plot title and/or the axis ranges). These commands should be
enclosed in braces, and each gnuplot command must be terminated with a
semi-colon. A backslash may be used to continue a set of gnuplot commands
over more than one line. Here is an example of the syntax:

	{ set title 'My Title'; set yrange [0:1000]; }

Menu path:    /View/Graph specified vars
Other access: Main window pop-up menu, graph button on toolbar

# graphpg Graphs

Variants:   graphpg add
            graphpg fontscale value
            graphpg show
            graphpg free
            graphpg --output=filename

The session "graph page" will work only if you have the LaTeX typesetting
system installed, and are able to generate and view PDF or PostScript
output.

In the session icon window, you can drag up to eight graphs onto the graph
page icon. When you double-click on the graph page (or right-click and
select "Display"), a page containing the selected graphs will be composed
and opened in a suitable viewer. From there you should be able to print the
page.

To clear the graph page, right-click on its icon and select "Clear".

Note that on systems other than MS Windows, you may have to adjust the
setting for the program used to view PDF or PostScript files. Find that
under the "Programs" tab in the gretl Preferences dialog box (under the
Tools menu in the main window).

It's also possible to operate on the graph page via script, or using the
console (in the GUI program). The following commands and options are
supported:

To add a graph to the graph page, issue the command graphpg add after saving
a named graph, as in

	grf1 <- gnuplot Y X
	graphpg add

To display the graph page: graphpg show.

To clear the graph page: graphpg free.

To adjust the scale of the font used in the graph page, use graphpg
fontscale scale, where scale is a multiplier (with a default of 1.0). Thus
to make the font size 50 percent bigger than the default you can do

	graphpg fontscale 1.5

To call for printing of the graph page to file, use the flag --output= plus
a filename; the filename should have the suffix ".pdf", ".ps" or ".eps". For
example:

	graphpg --output="myfile.pdf"

In this context the output uses colored lines by default; to use dot/dash
patterns instead of colors you can append the --monochrome flag.

# hausman Tests

This test is available only after estimating an OLS model using panel data
(see also "setobs"). It tests the simple pooled model against the principal
alternatives, the fixed effects and random effects models.

The fixed effects model allows the intercept of the regression to vary
across the cross-sectional units. An F-test is reported for the null
hypotheses that the intercepts do not differ. The random effects model
decomposes the residual variance into two parts, one part specific to the
cross-sectional unit and the other specific to the particular observation.
(This estimator can be computed only if the number of cross-sectional units
in the data set exceeds the number of parameters to be estimated.) The
Breusch-Pagan LM statistic tests the null hypothesis that the pooled OLS
estimator is adequate against the random effects alternative.

The pooled OLS model may be rejected against both of the alternatives, fixed
effects and random effects. Provided the unit- or group-specific error is
uncorrelated with the independent variables, the random effects estimator is
more efficient than the fixed effects estimator; otherwise the random
effects estimator is inconsistent and the fixed effects estimator is to be
preferred. The null hypothesis for the Hausman test is that the
group-specific error is not so correlated (and therefore the random effects
model is preferable). A low p-value for this test counts against the random
effects model and in favor of fixed effects.

Menu path:    Model window, /Tests/Panel diagnostics

# heckit Estimation

Arguments:  depvar indepvars ; selection equation 
Options:    --quiet (suppress printing of results)
            --robust (QML standard errors)
            --two-step (perform two-step estimation)
            --vcv (print covariance matrix)
            --verbose (print extra output)
Example:    heckit y 0 x1 x2 ; ys 0 x3 x4
            See also heckit.inp

Heckman-type selection model. In the specification, the list before the
semicolon represents the outcome equation, and the second list represents
the selection equation. The dependent variable in the selection equation (ys
in the example above) must be a binary variable.

By default, the parameters are estimated by maximum likelihood. The
covariance matrix of the parameters is computed using the negative inverse
of the Hessian. If two-step estimation is desired, use the --two-step
option. In this case, the covariance matrix of the parameters of the outcome
equation is appropriately adjusted as per Heckman (1979).

Please note that in ML estimation a numerical approximation of the Hessian
is used; this may lead to inaccuracies in the estimated covariance matrix if
the scale of the explanatory variables is such that some of the estimated
coefficients are very small in absolute value. This problem will be
addressed in future versions; in the meantime, rescaling the offending
explanatory variable(s) can be used as a workaround.

Menu path:    /Model/Limited dependent variable/Heckit

# help Utilities

Variants:   help
            help functions
            help command
            help function
Option:     --func (select functions help)

If no arguments are given, prints a list of available commands. If the
single argument "functions" is given, prints a list of available functions
(see "genr").

help command describes command (e.g. help smpl). help function describes
function (e.g. help ldet). Some functions have the same names as related
commands (e.g. diff): in that case the default is to print help for the
command, but you can get help on the function by using the --func option.

Menu path:    /Help

# hsk Estimation

Arguments:  depvar indepvars 
Option:     --vcv (print covariance matrix)

This command is applicable where heteroskedasticity is present in the form
of an unknown function of the regressors which can be approximated by a
quadratic relationship. In that context it offers the possibility of
consistent standard errors and more efficient parameter estimates as
compared with OLS.

The procedure involves (a) OLS estimation of the model of interest, followed
by (b) an auxiliary regression to generate an estimate of the error
variance, then finally (c) weighted least squares, using as weight the
reciprocal of the estimated variance.

In the auxiliary regression (b) we regress the log of the squared residuals
from the first OLS on the original regressors and their squares. The log
transformation is performed to ensure that the estimated variances are
non-negative. Call the fitted values from this regression u^*. The weight
series for the final WLS is then formed as 1/exp(u^*).

Menu path:    /Model/Other linear models/Heteroskedasticity corrected

# hurst Statistics

Argument:   series 

Calculates the Hurst exponent (a measure of persistence or long memory) for
a time-series variable having at least 128 observations.

The Hurst exponent is discussed by Mandelbrot. In theoretical terms it is
the exponent, H, in the relationship

  RS(x) = an^H

where RS is the "rescaled range" of the variable x in samples of size n and
a is a constant. The rescaled range is the range (maximum minus minimum) of
the cumulated value or partial sum of x over the sample period (after
subtraction of the sample mean), divided by the sample standard deviation.

As a reference point, if x is white noise (zero mean, zero persistence) then
the range of its cumulated "wandering" (which forms a random walk), scaled
by the standard deviation, grows as the square root of the sample size,
giving an expected Hurst exponent of 0.5. Values of the exponent
significantly in excess of 0.5 indicate persistence, and values less than
0.5 indicate anti-persistence (negative autocorrelation). In principle the
exponent is bounded by 0 and 1, although in finite samples it is possible to
get an estimated exponent greater than 1.

In gretl, the exponent is estimated using binary sub-sampling: we start with
the entire data range, then the two halves of the range, then the four
quarters, and so on. For sample sizes smaller than the data range, the RS
value is the mean across the available samples. The exponent is then
estimated as the slope coefficient in a regression of the log of RS on the
log of sample size.

Menu path:    /Variable/Hurst exponent

# if Programming

Flow control for command execution. Three sorts of construction are
supported, as follows.

	# simple form
	if condition
	    commands
	endif

	# two branches
	if condition
	    commands1
	else
	    commands2
        endif

	# three or more branches
	if condition1
	    commands1
	elif condition2
	    commands2
	else
	    commands3
	endif

"condition" must be a Boolean expression, for the syntax of which see
"genr". More than one "elif" block may be included. In addition, if ...
endif blocks may be nested.

# include Programming

Argument:   filename 
Examples:   include myfile.inp
            include sols.gfn

Intended for use in a command script, primarily for including definitions of
functions. Executes the commands in filename then returns control to the
main script. To include a packaged function, be sure to include the filename
extension.

See also "run".

# info Dataset

Prints out any supplementary information stored with the current datafile.

Menu path:    /Data/Dataset info
Other access: Data browser windows

# intreg Estimation

Arguments:   minvar maxvar indepvars 
Options:    --quiet (suppress printing of results)
            --verbose (print details of iterations)
            --robust (robust standard errors)
            --cluster=clustvar (see "logit" for explanation)
Example:    intreg lo hi const x1 x2
            See also wtp.inp

Estimates an interval regression model. This model arises when the dependent
variable is imperfectly observed for some (possibly all) observations. In
other words, the data generating process is assumed to be

  y* = x b + u

but we only observe m <= y* <= M (the interval may be left- or
right-unbounded). Note that for some observations m may equal M. The
variables minvar and maxvar must contain NAs for left- and right-unbounded
observations, respectively.

The model is estimated by maximum likelihood, assuming normality of the
disturbance term.

By default, standard errors are computed using the negative inverse of the
Hessian. If the --robust flag is given, then QML or Huber-White standard
errors are calculated instead. In this case the estimated covariance matrix
is a "sandwich" of the inverse of the estimated Hessian and the outer
product of the gradient.

Menu path:    /Model/Limited dependent variable/Interval regression

# join Dataset

Arguments:  filename varname 
Options:    --data=column-name (see below)
            --filter=expression (see below)
            --ikey=inner-key (see below)
            --okey=outer-key (see below)
            --aggr=method (see below)
            --tkey=column-name,format-string (see below)

This command imports a data series from the source filename (which must be a
delimited text data file) under the name varname. For details please see the
Gretl User's Guide; here we just give a brief summary of the available
options.

The --data option can be used to specify the column heading of the data in
the source file, if this differs from the name by which the data should be
known in gretl.

The --filter option can be used to specify a criterion for filtering the
source data (that is, selecting a subset of observations).

The --ikey and --okey options can be used to specify a mapping between
observations in the current dataset and observations in the source data (for
example, individuals can be matched against the household to which they
belong).

The --aggr option is used when the mapping between observations in the
current dataset and the source is not one-to-one.

The --tkey option is applicable only when the current dataset has a
time-series structure. It can be used to specify the name of a column
containing dates to be matched to the dataset and/or the format in which
dates are represented in that column.

See also "append" for simpler joining operations.

# kalman Estimation

Options:    --cross (allow for cross-correlated disturbances)
            --diffuse (use diffuse initialization)

Opens a block of statements to set up a Kalman filter. This block should end
with the line end kalman, to which the options shown above may be appended.
The intervening lines specify the matrices that compose the filter. For
example,

	kalman 
	  obsy y
	  obsymat H
	  statemat F
	  statevar Q
	end kalman

Please see the Gretl User's Guide for details.

See also "kfilter", "ksimul", "ksmooth".

# kpss Tests

Arguments:  order varlist 
Options:    --trend (include a trend)
            --seasonals (include seasonal dummies)
            --verbose (print regression results)
            --quiet (suppress printing of results)
            --difference (use first difference of variable)
Examples:   kpss 8 y
            kpss 4 x1 --trend

For use of this command with panel data please see the final section in this
entry.

Computes the KPSS test (Kwiatkowski et al, Journal of Econometrics, 1992)
for stationarity, for each of the specified variables (or their first
difference, if the --difference option is selected). The null hypothesis is
that the variable in question is stationary, either around a level or, if
the --trend option is given, around a deterministic linear trend.

The order argument determines the size of the window used for Bartlett
smoothing. If the --verbose option is chosen the results of the auxiliary
regression are printed, along with the estimated variance of the random walk
component of the variable.

The critical values shown for the test statistic are based on the response
surfaces estimated by Sephton (Economics Letters, 1995), which are more
accurate for small samples than the values given in the original KPSS
article. When the test statistic lies between the 10 percent and 1 percent
critical values a p-value is shown; this is obtained by linear interpolation
and should not be taken too literally.

Panel data

When the kpss command is used with panel data, to produce a panel unit root
test, the applicable options and the results shown are somewhat different.
While you may give a list of variables for testing in the regular
time-series case, with panel data only one variable may be tested per
command. And the --verbose option has a different meaning: it produces a
brief account of the test for each individual time series (the default being
to show only the overall result).

When possible, the overall test (null hypothesis: the series in question is
stationary for all the panel units) is calculated using the method of Choi
(Journal of International Money and Finance, 2001). This is not always
straightforward, the difficulty being that while the Choi test is based on
the p-values of the tests on the individual series, we do not currently have
a means of calculating p-values for the KPSS test statistic; we must rely on
a few critical values.

If the test statistic for a given series falls between the 10 percent and 1
percent critical values, we are able to interpolate a p-value. But if the
test falls short of the 10 percent value, or exceeds the 1 percent value, we
cannot interpolate and can at best place a bound on the global Choi test. If
the individual test statistic falls short of the 10 percent value for some
units but exceeds the 1 percent value for others, we cannot even compute a
bound for the global test.

Menu path:    /Variable/Unit root tests/KPSS test

# labels Dataset

Variants:   labels [ varlist ]
            labels --to-file=filename
            labels --from-file=filename
            labels --delete

In the first form, prints out the informative labels (if present) for the
series in varlist, or for all series in the dataset if varlist is not
specified.

With the option --to-file, writes to the named file the labels for all
series in the dataset, one per line. If no labels are present an error is
flagged; if some series have labels and others do not, a blank line is
printed for series with no label.

With the option --from-file, reads the specified file (which should be plain
text) and assigns labels to the series in the dataset, reading one label per
line and taking blank lines to indicate blank labels.

The --delete option does what you'd expect: it removes all the series labels
from the dataset.

Menu path:    /Data/Variable labels

# lad Estimation

Arguments:  depvar indepvars 
Option:     --vcv (print covariance matrix)

Calculates a regression that minimizes the sum of the absolute deviations of
the observed from the fitted values of the dependent variable. Coefficient
estimates are derived using the Barrodale-Roberts simplex algorithm; a
warning is printed if the solution is not unique.

Standard errors are derived using the bootstrap procedure with 500 drawings.
The covariance matrix for the parameter estimates, printed when the --vcv
flag is given, is based on the same bootstrap.

Menu path:    /Model/Robust estimation/Least Absolute Deviation

# lags Transformations

Arguments:  [ order ; ] laglist 
Examples:   lags x y
            lags 12 ; x y

Creates new series which are lagged values of each of the series in varlist.
By default the number of lags created equals the periodicity of the data.
For example, if the periodicity is 4 (quarterly), the command "lags x"
creates

	x_1 = x(t-1)
	x_2 = x(t-2)
	x_3 = x(t-3)
	x_4 = x(t-4)

The number of lags created can be controlled by the optional first parameter
(which, if present, must be followed by a semicolon).

Menu path:    /Add/Lags of selected variables

# ldiff Transformations

Argument:   varlist 

The first difference of the natural log of each series in varlist is
obtained and the result stored in a new series with the prefix ld_. Thus
"ldiff x y" creates the new variables

	ld_x = log(x) - log(x(-1))
	ld_y = log(y) - log(y(-1))

Menu path:    /Add/Log differences of selected variables

# leverage Tests

Options:    --save (save variables)
            --quiet (don't print results)

Must follow an "ols" command. Calculates the leverage (h, which must lie in
the range 0 to 1) for each data point in the sample on which the previous
model was estimated. Displays the residual (u) for each observation along
with its leverage and a measure of its influence on the estimates, uh/(1 -
h). "Leverage points" for which the value of h exceeds 2k/n (where k is the
number of parameters being estimated and n is the sample size) are flagged
with an asterisk. For details on the concepts of leverage and influence see
Davidson and MacKinnon (1993), Chapter 2.

DFFITS values are also computed: these are "studentized residuals"
(predicted residuals divided by their standard errors) multiplied by
sqrt[h/(1 - h)]. For discussions of studentized residuals and DFFITS see
chapter 12 of Maddala's Introduction to Econometrics or Belsley, Kuh and
Welsch (1980).

Briefly, a "predicted residual" is the difference between the observed value
of the dependent variable at observation t, and the fitted value for
observation t obtained from a regression in which that observation is
omitted (or a dummy variable with value 1 for observation t alone has been
added); the studentized residual is obtained by dividing the predicted
residual by its standard error.

If the --save flag is given with this command, then the leverage, influence
and DFFITS values are added to the current data set. In that context the
--quiet flag may be used to suppress the printing of results.

After execution, the $test accessor returns the cross-validation criterion,
which is defined as the sum of squared deviations of the dependent variable
from its forecast value, the forecast for each observation being based on a
sample from which that observation is excluded. (This is known as the
leave-one-out estimator). For a broader discussion of the cross-validation
criterion, see Davidson and MacKinnon's Econometric Theory and Methods,
pages 685-686, and the references therein.

Menu path:    Model window, /Tests/Influential observations

# levinlin Tests

Arguments:  order series 
Options:    --nc (test without a constant)
            --ct (with constant and trend)
            --quiet (suppress printing of results)
Examples:   levinlin 0 y
            levinlin 2 y --ct
            levinlin {2,2,3,3,4,4} y

Carries out the panel unit-root test described by Levin, Lin and Chu (2002).
The null hypothesis is that all of the individual time series exhibit a unit
root, and the alternative is that none of the series has a unit root. (That
is, a common AR(1) coefficient is assumed, although in other respects the
statistical properties of the series are allowed to vary across
individuals.)

By default the test ADF regressions include a constant; to suppress the
constant use the --nc option, or to add a linear trend use the --ct option.
(See the "adf" command for explanation of ADF regressions.)

The (non-negative) order for the test (governing the number of lags of the
dependent variable to include in the ADF regressions) may be given in either
of two forms. If a scalar value is given, this is applied to all the
individuals in the panel. The alternative is to provide a matrix containing
a specific lag order for each individual; this must be a vector with as many
elements as there are individuals in the current sample range. Such a matrix
can be specified by name, or constructed using braces as illustrated in the
last example above.

Menu path:    /Variable/Unit root tests/Levin-Lin-Chu test

# logistic Estimation

Arguments:  depvar indepvars 
Options:    --ymax=value (specify maximum of dependent variable)
            --vcv (print covariance matrix)
Examples:   logistic y const x
            logistic y const x --ymax=50

Logistic regression: carries out an OLS regression using the logistic
transformation of the dependent variable,

  log(y/(y* - y))

The dependent variable must be strictly positive. If all its values lie
between 0 and 1, the default is to use a y^* value (the asymptotic maximum
of the dependent variable) of 1; if its values lie between 0 and 100, the
default y^* is 100.

If you wish to set a different maximum, use the --ymax option. Note that the
supplied value must be greater than all of the observed values of the
dependent variable.

The fitted values and residuals from the regression are automatically
transformed using

  y = y* / (1 + exp(-x))

where x represents either a fitted value or a residual from the OLS
regression using the transformed dependent variable. The reported values are
therefore comparable with the original dependent variable.

Note that if the dependent variable is binary, you should use the "logit"
command instead.

Menu path:    /Model/Limited dependent variable/Logistic

# logit Estimation

Arguments:  depvar indepvars 
Options:    --robust (robust standard errors)
            --cluster=clustvar (clustered standard errors)
            --multinomial (estimate multinomial logit)
            --vcv (print covariance matrix)
            --verbose (print details of iterations)
            --p-values (show p-values instead of slopes)

If the dependent variable is a binary variable (all values are 0 or 1)
maximum likelihood estimates of the coefficients on indepvars are obtained
via the Newton-Raphson method. As the model is nonlinear the slopes depend
on the values of the independent variables. By default the slopes with
respect to each of the independent variables are calculated (at the means of
those variables) and these slopes replace the usual p-values in the
regression output. This behavior can be suppressed my giving the --p-values
option. The chi-square statistic tests the null hypothesis that all
coefficients are zero apart from the constant.

By default, standard errors are computed using the negative inverse of the
Hessian. If the --robust flag is given, then QML or Huber-White standard
errors are calculated instead. In this case the estimated covariance matrix
is a "sandwich" of the inverse of the estimated Hessian and the outer
product of the gradient; see chapter 10 of Davidson and MacKinnon (2004).
But if the --cluster option is given, then "cluster-robust" standard errors
are produced; see the Gretl User's Guide for details.

If the dependent variable is not binary but is discrete, then by default it
is interpreted as an ordinal response, and Ordered Logit estimates are
obtained. However, if the --multinomial option is given, the dependent
variable is interpreted as an unordered response, and Multinomial Logit
estimates are produced. (In either case, if the variable selected as
dependent is not discrete an error is flagged.) In the multinomial case, the
accessor $mnlprobs is available after estimation, to get a matrix containing
the estimated probabilities of the outcomes at each observation
(observations in rows, outcomes in columns).

If you want to use logit for analysis of proportions (where the dependent
variable is the proportion of cases having a certain characteristic, at each
observation, rather than a 1 or 0 variable indicating whether the
characteristic is present or not) you should not use the "logit" command,
but rather construct the logit variable, as in

	series lgt_p = log(p/(1 - p))

and use this as the dependent variable in an OLS regression. See chapter 12
of Ramanathan (2002).

Menu path:    /Model/Limited dependent variable/Logit

# logs Transformations

Argument:   varlist 

The natural log of each of the series in varlist is obtained and the result
stored in a new series with the prefix l_ ("el" underscore). For example,
"logs x y" creates the new variables l_x = ln(x) and l_y = ln(y).

Menu path:    /Add/Logs of selected variables

# loop Programming

Argument:   control 
Options:    --progressive (enable special forms of certain commands)
            --verbose (report details of genr commands)
            --quiet (do not report number of iterations performed)
Examples:   loop 1000
            loop 1000 --progressive
            loop while essdiff > .00001
            loop i=1991..2000
            loop for (r=-.99; r<=.99; r+=.01)
            loop foreach i xlist

This command opens a special mode in which the program accepts commands to
be executed repeatedly. You exit the mode of entering loop commands with
"endloop": at this point the stacked commands are executed.

The parameter "control" may take any of five forms, as shown in the
examples: an integer number of times to repeat the commands within the loop;
"while" plus a boolean condition; a range of integer values for index
variable; "for" plus three expressions in parentheses, separated by
semicolons (which emulates the for statement in the C programming language);
or "foreach" plus an index variable and a list.

See the Gretl User's Guide for further details and examples. The effect of
the --progressive option (which is designed for use in Monte Carlo
simulations) is explained there. Not all gretl commands may be used within a
loop; the commands available in this context are also set out there.

# mahal Statistics

Argument:   varlist 
Options:    --quiet (don't print anything)
            --save (add distances to the dataset)
            --vcv (print covariance matrix)

Computes the Mahalanobis distances between the series in varlist. The
Mahalanobis distance is the distance between two points in a k-dimensional
space, scaled by the statistical variation in each dimension of the space.
For example, if p and q are two observations on a set of k variables with
covariance matrix C, then the Mahalanobis distance between the observations
is given by

  sqrt((p - q)' * C-inverse * (p - q))

where (p - q) is a k-vector. This reduces to Euclidean distance if the
covariance matrix is the identity matrix.

The space for which distances are computed is defined by the selected
variables. For each observation in the current sample range, the distance is
computed between the observation and the centroid of the selected variables.
This distance is the multidimensional counterpart of a standard z-score, and
can be used to judge whether a given observation "belongs" with a group of
other observations.

If the --vcv option is given, the covariance matrix and its inverse are
printed. If the --save option is given, the distances are saved to the
dataset under the name mdist (or mdist1, mdist2 and so on if there is
already a variable of that name).

Menu path:    /View/Mahalanobis distances

# makepkg Programming

Argument:   filename 
Options:    --index (write auxiliary index file)
            --translations (write auxiliary strings file)

Supports creation of a gretl function package via the command line. The
filename argument represents the name of the package to be created, and
should have the .gfn extension. Please see the Gretl User's Guide for
details.

The option flags support the writing of auxiliary files for use with gretl
"addons". The index file is a short XML document containing basic
information about the package; it has the same basename as the package and
the extension .xml. The translations file contains strings from the package
that may be suitable for translation, in C format; for package foo this file
is named foo-i18n.c.

Menu path:    /File/Function files/New package

# markers Dataset

Variants:   markers --to-file=filename
            markers --from-file=filename
            markers --delete

With the option --to-file, writes to the named file the observation marker
strings from the current dataset, one per line. If no such strings are
present an error is flagged.

With the option --from-file, reads the specified file (which should be plain
text) and assigns observation markers to the rows in the dataset, reading
one marker per line. In general there should be at least as many markers in
the file as observations in the dataset, but if the dataset is a panel it is
also acceptable if the number of markers in the file matches the number of
cross-sectional units (in which case the markers are repeated for each time
period.)

The --delete option does what you'd expect: it removes the observation
marker strings from the dataset.

Menu path:    /Data/Observation markers

# meantest Tests

Arguments:  series1 series2 
Option:     --unequal-vars (assume variances are unequal)

Calculates the t statistic for the null hypothesis that the population means
are equal for the variables series1 and series2, and shows its p-value.

By default the test statistic is calculated on the assumption that the
variances are equal for the two variables; with the --unequal-vars option
the variances are assumed to be different. This will make a difference to
the test statistic only if there are different numbers of non-missing
observations for the two series.

Menu path:    /Tools/Test statistic calculator

# mle Estimation

Arguments:  log-likelihood function [ derivatives ] 
Options:    --quiet (don't show estimated model)
            --vcv (print covariance matrix)
            --hessian (base covariance matrix on the Hessian)
            --robust (QML covariance matrix)
            --verbose (print details of iterations)
            --no-gradient-check (see below)
            --lbfgs (use L-BFGS-B instead of regular BFGS)
Example:    weibull.inp

Performs Maximum Likelihood (ML) estimation using either the BFGS (Broyden,
Fletcher, Goldfarb, Shanno) algorithm or Newton's method. The user must
specify the log-likelihood function. The parameters of this function must be
declared and given starting values (using the "genr" command) prior to
estimation. Optionally, the user may specify the derivatives of the
log-likelihood function with respect to each of the parameters; if
analytical derivatives are not supplied, a numerical approximation is
computed.

Simple example: Suppose we have a series X with values 0 or 1 and we wish to
obtain the maximum likelihood estimate of the probability, p, that X = 1.
(In this simple case we can guess in advance that the ML estimate of p will
simply equal the proportion of Xs equal to 1 in the sample.)

The parameter p must first be added to the dataset and given an initial
value. For example, scalar p = 0.5.

We then construct the MLE command block:

	mle loglik = X*log(p) + (1-X)*log(1-p)
	  deriv p = X/p - (1-X)/(1-p)
	end mle

The first line above specifies the log-likelihood function. It starts with
the keyword mle, then a dependent variable is specified and an expression
for the log-likelihood is given (using the same syntax as in the "genr"
command). The next line (which is optional) starts with the keyword deriv
and supplies the derivative of the log-likelihood function with respect to
the parameter p. If no derivatives are given, you should include a statement
using the keyword params which identifies the free parameters: these are
listed on one line, separated by spaces and can be either scalars, or
vectors, or any combination of the two. For example, the above could be
changed to:

	mle loglik = X*log(p) + (1-X)*log(1-p)
	  params p
	end mle

in which case numerical derivatives would be used.

Note that any option flags should be appended to the ending line of the MLE
block.

By default, estimated standard errors are based on the Outer Product of the
Gradient. If the --hessian option is given, they are instead based on the
negative inverse of the Hessian (which is approximated numerically). If the
--robust option is given, a QML estimator is used (namely, a sandwich of the
negative inverse of the Hessian and the covariance matrix of the gradient).

If you supply analytical derivatives, by default gretl runs a numerical
check on their plausibility. Occasionally this may produce false positives,
instances where correct derivatives appear to be wrong and estimation is
refused. To counter this, or to achieve a little extra speed, you can give
the option --no-gradient-check. Obviously, you should do this only if you
are quite confident that the gradient you have specified is right.

For a much more in-depth description of "mle", please refer to the Gretl
User's Guide.

Menu path:    /Model/Maximum likelihood

# modeltab Utilities

Variants:   modeltab add
            modeltab show
            modeltab free
            modeltab --output=filename

Manipulates the gretl "model table". See the Gretl User's Guide for details.
The sub-commands have the following effects: "add" adds the last model
estimated to the model table, if possible; "show" displays the model table
in a window; and "free" clears the table.

To call for printing of the model table, use the flag --output= plus a
filename. If the filename has the suffix ".tex", the output will be in TeX
format; if the suffix is ".rtf" the output will be RTF; otherwise it will be
plain text. In the case of TeX output the default is to produce a
"fragment", suitable for inclusion in a document; if you want a stand-alone
document instead, use the --complete option, for example

	modeltab --output="myfile.tex" --complete

Menu path:    Session icon window, Model table icon

# modprint Printing

Arguments:  coeffmat names [ addstats ] 

Prints the coefficient table and optional additional statistics for a model
estimated "by hand". Mainly useful for user-written functions.

The argument coeffmat should be a k by 2 matrix containing k coefficients
and k associated standard errors, and names should be a string containing at
least k names for the coefficients, separated by commas or spaces. (The
names argument may be either the name of a string variable or a literal
string, enclosed in double quotes.)

The optional argument addstats is a vector containing p additional
statistics to be printed under the coefficient table. If this argument is
given, then names should contain k + p comma-separated strings, the
additional p strings to be associated with the additional statistics.

# modtest Tests

Argument:   [ order ] 
Options:    --normality (normality of residual)
            --logs (non-linearity, logs)
            --autocorr (serial correlation)
            --arch (ARCH)
            --squares (non-linearity, squares)
            --white (heteroskedasticity, White's test)
            --white-nocross (White's test, squares only)
            --breusch-pagan (heteroskedasticity, Breusch-Pagan)
            --robust (robust variance estimate for Breusch-Pagan)
            --panel (heteroskedasticity, groupwise)
            --comfac (common factor restriction, AR1 models only)
            --quiet (don't print details)

Must immediately follow an estimation command. Depending on the option
given, this command carries out one of the following: the Doornik-Hansen
test for the normality of the error term; a Lagrange Multiplier test for
nonlinearity (logs or squares); White's test (with or without
cross-products) or the Breusch-Pagan test (Breusch and Pagan, 1979) for
heteroskedasticity; the LMF test for serial correlation (Kiviet, 1986); a
test for ARCH (Autoregressive Conditional Heteroskedasticity; see also the
"arch" command); or a test of the common factor restriction implied by AR(1)
estimation. With the exception of the normality and common factor test most
of the options are only available for models estimated via OLS, but see
below for details regarding two-stage least squares.

The optional order argument is relevant only in case the --autocorr or
--arch options are selected. The default is to run these tests using a lag
order equal to the periodicity of the data, but this can be adjusted by
supplying a specific lag order.

The --robust option applies only when the Breusch-Pagan test is selected;
its effect is to use the robust variance estimator proposed by Koenker
(1981), making the test less sensitive to the assumption of normality.

The --panel option is available only when the model is estimated on panel
data: in this case a test for groupwise heteroskedasticity is performed
(that is, for a differing error variance across the cross-sectional units).

The --comfac option is available only when the model is estimated via an
AR(1) method such as Hildreth-Lu. The auxiliary regression takes the form of
a relatively unrestricted dynamic model, which is used to test the common
factor restriction implicit in the AR(1) specification.

By default, the program prints the auxiliary regression on which the test
statistic is based, where applicable. This may be suppressed by using the
--quiet flag. The test statistic and its p-value may be retrieved using the
accessors $test and $pvalue respectively.

When a model has been estimated by two-stage least squares (see "tsls"), the
LM principle breaks down and gretl offers some equivalents: the --autocorr
option computes Godfrey's test for autocorrelation (Godfrey, 1994) while the
--white option yields the HET1 heteroskedasticity test (Pesaran and Taylor,
1999).

Menu path:    Model window, /Tests

# mpols Estimation

Arguments:  depvar indepvars 
Options:    --vcv (print covariance matrix)
            --simple-print (do not print auxiliary statistics)
            --quiet (suppress printing of results)

Computes OLS estimates for the specified model using multiple precision
floating-point arithmetic, with the help of the Gnu Multiple Precision (GMP)
library. By default 256 bits of precision are used for the calculations, but
this can be increased via the environment variable GRETL_MP_BITS. For
example, when using the bash shell one could issue the following command,
before starting gretl, to set a precision of 1024 bits.

	export GRETL_MP_BITS=1024

A rather arcane option is available for this command (primarily for testing
purposes): if the indepvars list is followed by a semicolon and a further
list of numbers, those numbers are taken as powers of x to be added to the
regression, where x is the last variable in indepvars. These additional
terms are computed and stored in multiple precision. In the following
example y is regressed on x and the second, third and fourth powers of x:

	mpols y 0 x ; 2 3 4

Menu path:    /Model/Other linear models/High precision OLS

# negbin Estimation

Arguments:  depvar indepvars [ ; offset ] 
Options:    --model1 (use NegBin 1 model)
            --robust (QML covariance matrix)
            --cluster=clustvar (see "logit" for explanation)
            --opg (see below)
            --vcv (print covariance matrix)
            --verbose (print details of iterations)

Estimates a Negative Binomial model. The dependent variable is taken to
represent a count of the occurrence of events of some sort, and must have
only non-negative integer values. By default the model NegBin 2 is used, in
which the conditional variance of the count is given by mu(1 + αmu), where
mu denotes the conditional mean. But if the --model1 option is given the
conditional variance is mu(1 + α).

The optional offset series works in the same way as for the "poisson"
command. The Poisson model is a restricted form of the Negative Binomial in
which α = 0 by construction.

By default, standard errors are computed using a numerical approximation to
the Hessian at convergence. But if the --opg option is given the covariance
matrix is based on the Outer Product of the Gradient (OPG), or if the
--robust option is given QML standard errors are calculated, using a
"sandwich" of the inverse of the Hessian and the OPG.

Menu path:    /Model/Limited dependent variable/Count data...

# nls Estimation

Arguments:  function [ derivatives ] 
Options:    --quiet (don't show estimated model)
            --robust (robust standard errors)
            --vcv (print covariance matrix)
            --verbose (print details of iterations)
Example:    wg_nls.inp

Performs Nonlinear Least Squares (NLS) estimation using a modified version
of the Levenberg-Marquardt algorithm. You must supply a function
specification. The parameters of this function must be declared and given
starting values (using the "genr" command) prior to estimation. Optionally,
you may specify the derivatives of the regression function with respect to
each of the parameters. If you do not supply derivatives you should instead
give a list of the parameters to be estimated (separated by spaces or
commas), preceded by the keyword params. In the latter case a numerical
approximation to the Jacobian is computed.

It is easiest to show what is required by example. The following is a
complete script to estimate the nonlinear consumption function set out in
William Greene's Econometric Analysis (Chapter 11 of the 4th edition, or
Chapter 9 of the 5th). The numbers to the left of the lines are for
reference and are not part of the commands. Note that any option flags, such
as --vcv for printing the covariance matrix of the parameter estimates,
should be appended to the final command, end nls.

	1   open greene11_3.gdt
	2   ols C 0 Y
	3   scalar a = $coeff(0)
	4   scalar b = $coeff(Y)
	5   scalar g = 1.0
	6   nls C = a + b * Y^g
	7    deriv a = 1
	8    deriv b = Y^g
	9    deriv g = b * Y^g * log(Y)
	10  end nls --vcv

It is often convenient to initialize the parameters by reference to a
related linear model; that is accomplished here on lines 2 to 5. The
parameters alpha, beta and gamma could be set to any initial values (not
necessarily based on a model estimated with OLS), although convergence of
the NLS procedure is not guaranteed for an arbitrary starting point.

The actual NLS commands occupy lines 6 to 10. On line 6 the "nls" command is
given: a dependent variable is specified, followed by an equals sign,
followed by a function specification. The syntax for the expression on the
right is the same as that for the "genr" command. The next three lines
specify the derivatives of the regression function with respect to each of
the parameters in turn. Each line begins with the keyword "deriv", gives the
name of a parameter, an equals sign, and an expression whereby the
derivative can be calculated (again, the syntax here is the same as for
"genr"). As an alternative to supplying numerical derivatives, you could
substitute the following for lines 7 to 9:

	params a b g

Line 10, "end nls", completes the command and calls for estimation. Any
options should be appended to this line.

For further details on NLS estimation please see the Gretl User's Guide.

Menu path:    /Model/Nonlinear Least Squares

# normtest Tests

Argument:   series 
Options:    --dhansen (Doornik-Hansen test, the default)
            --swilk (Shapiro-Wilk test)
            --lillie (Lilliefors test)
            --jbera (Jarque-Bera test)
            --all (do all tests)
            --quiet (suppress printed output)

Carries out a test for normality for the given series. The specific test is
controlled by the option flags (but if no flag is given, the Doornik-Hansen
test is performed). Note: the Doornik-Hansen and Shapiro-Wilk tests are
recommended over the others, on account of their superior small-sample
properties.

The test statistic and its p-value may be retrieved using the accessors
$test and $pvalue. Please note that if the --all option is given, the result
recorded is that from the Doornik-Hansen test.

Menu path:    /Variable/Normality test

# nulldata Dataset

Argument:   series_length 
Option:     --preserve (preserve matrices)
Example:    nulldata 500

Establishes a "blank" data set, containing only a constant and an index
variable, with periodicity 1 and the specified number of observations. This
may be used for simulation purposes: some of the "genr" commands (e.g. "genr
uniform()", "genr normal()") will generate dummy data from scratch to fill
out the data set. This command may be useful in conjunction with "loop". See
also the "seed" option to the "set" command.

By default, this command cleans out all data in gretl's current workspace.
If you give the --preserve option, however, any currently defined matrices
are retained.

Menu path:    /File/New data set

# ols Estimation

Arguments:  depvar indepvars 
Options:    --vcv (print covariance matrix)
            --robust (robust standard errors)
            --cluster=clustvar (clustered standard errors)
            --jackknife (see below)
            --simple-print (do not print auxiliary statistics)
            --quiet (suppress printing of results)
            --anova (print an ANOVA table)
            --no-df-corr (suppress degrees of freedom correction)
            --print-final (see below)
Examples:   ols 1 0 2 4 6 7
            ols y 0 x1 x2 x3 --vcv
            ols y 0 x1 x2 x3 --quiet

Computes ordinary least squares (OLS) estimates with depvar as the dependent
variable and indepvars as the list of independent variables. Variables may
be specified by name or number; use the number zero for a constant term.

Besides coefficient estimates and standard errors, the program also prints
p-values for t (two-tailed) and F-statistics. A p-value below 0.01 indicates
statistical significance at the 1 percent level and is marked with ***. **
indicates significance between 1 and 5 percent and * indicates significance
between the 5 and 10 percent levels. Model selection statistics (the Akaike
Information Criterion or AIC and Schwarz's Bayesian Information Criterion)
are also printed. The formula used for the AIC is that given by Akaike
(1974), namely minus two times the maximized log-likelihood plus two times
the number of parameters estimated.

If the option --no-df-corr is given, the usual degrees of freedom correction
is not applied when calculating the estimated error variance (and hence also
the standard errors of the parameter estimates).

The option --print-final is applicable only in the context of a "loop". It
arranges for the regression to be run silently on all but the final
iteration of the loop. See the Gretl User's Guide for details.

Various internal variables may be retrieved following estimation. For
example

	series uh = $uhat

saves the residuals under the name uh. See the "accessors" section of the
gretl function reference for details.

The specific formula ("HC" version) used for generating robust standard
errors when the --robust option is given can be adjusted via the "set"
command. The --jackknife option has the effect of selecting an hc_version of
3a. The --cluster overrides the selection of HC version, and produces robust
standard errors by grouping the observations by the distinct values of
clustvar; see the Gretl User's Guide for details.

Menu path:    /Model/Ordinary Least Squares
Other access: Beta-hat button on toolbar

# omit Tests

Argument:   varlist 
Options:    --test-only (don't replace the current model)
            --chi-square (give chi-square form of Wald test)
            --quiet (print only the basic test result)
            --silent (don't print anything)
            --vcv (print covariance matrix for reduced model)
            --auto[=alpha] (sequential elimination, see below)
Examples:   omit 5 7 9
            omit seasonals --quiet
            omit --auto
            omit --auto=0.05

This command must follow an estimation command. It calculates a Wald test
for the joint significance of the variables in varlist, which should be a
subset of the independent variables in the model last estimated. The results
of the test may be retrieved using the accessors $test and $pvalue.

By default the restricted model is estimated and it replaces the original as
the "current model" for the purposes of, for example, retrieving the
residuals as $uhat or doing further tests. This behavior may be suppressed
via the --test-only option.

By default the F-form of the Wald test is recorded; the --chi-square option
may be used to record the chi-square form instead.

If the restricted model is both estimated and printed, the --vcv option has
the effect of printing its covariance matrix, otherwise this option is
ignored.

Alternatively, if the --auto flag is given, sequential elimination is
performed: at each step the variable with the highest p-value is omitted,
until all remaining variables have a p-value no greater than some cutoff.
The default cutoff is 10 percent (two-sided); this can be adjusted by
appending "=" and a value between 0 and 1 (with no spaces), as in the fourth
example above. If varlist is given this process is confined to the listed
variables, otherwise all variables are treated as candidates for omission.
Note that the --auto and --test-only options cannot be combined.

Menu path:    Model window, /Tests/Omit variables

# open Dataset

Argument:   filename 
Options:    --quiet (don't print list of series)
            --preserve (preserve any matrices and scalars)
            --www (use a database on the gretl server)
            See below for additional specialized options
Examples:   open data4-1
            open voter.dta
            open fedbog --www

Opens a data file. If a data file is already open, it is replaced by the
newly opened one. To add data to the current dataset, see "append" and (for
greater flexibility) "join".

If a full path is not given, the program will search some relevant paths to
try to find the file. If no filename suffix is given (as in the first
example above), gretl assumes a native datafile with suffix .gdt. Based on
the name of the file and various heuristics, gretl will try to detect the
format of the data file (native, plain text, CSV, MS Excel, Stata, SPSS,
etc.).

If the filename argument takes the form of a URI starting with http://, then
gretl will attempt to download the indicated data file before opening it.

By default, opening a new data file clears the current gretl session, which
includes deletion of any named matrices and scalars. If you wish to keep any
currently defined matrices and scalars, use the --preserve option.

The open command can also be used to open a database (gretl, RATS 4.0 or
PcGive) for reading. In that case it should be followed by the "data"
command to extract particular series from the database. If the www option is
given, the program will try to access a database of the given name on the
gretl server -- for instance the Federal Reserve interest rates database in
the third example above.

When opening a spreadsheet file (Gnumeric, Open Document or MS Excel), you
may give up to three additional parameters following the filename. First,
you can select a particular worksheet within the file. This is done either
by giving its (1-based) number, using the syntax, e.g., --sheet=2, or, if
you know the name of the sheet, by giving the name in double quotes, as in
--sheet="MacroData". The default is to read the first worksheet. You can
also specify a column and/or row offset into the worksheet via, e.g.,

	--coloffset=3 --rowoffset=2

which would cause gretl to ignore the first 3 columns and the first 2 rows.
The default is an offset of 0 in both dimensions, that is, to start reading
at the top-left cell.

With plain text files, gretl generally expects to find the data columns
delimited in some standard manner. But there is also a special facility for
reading "fixed format" files, in which there are no delimiters but there is
a known specification of the form, e.g., "variable k occupies 8 columns
starting at column 24". To read such files, you should append a string
--fixed-cols=colspec, where colspec is composed of comma-separated integers.
These integers are interpreted as a set of pairs. The first element of each
pair denotes a starting column, measured in bytes from the beginning of the
line with 1 indicating the first byte; and the second element indicates how
many bytes should be read for the given field. So, for example, if you say

	open fixed.txt --fixed-cols=1,6,20,3

then for variable 1 gretl will read 6 bytes starting at column 1; and for
variable 2, 3 bytes starting at column 20. Lines that are blank, or that
begin with #, are ignored, but otherwise the column-reading template is
applied, and if anything other than a valid numerical value is found an
error is flagged. If the data are read successfully, the variables will be
named v1, v2, etc. It's up to the user to provide meaningful names and/or
descriptions using the commands "rename" and/or "setinfo".

Menu path:    /File/Open data
Other access: Drag a data file into gretl (MS Windows or Gnome)

# orthdev Transformations

Argument:   varlist 

Applicable with panel data only. A series of forward orthogonal deviations
is obtained for each variable in varlist and stored in a new variable with
the prefix o_. Thus "orthdev x y" creates the new variables o_x and o_y.

The values are stored one step ahead of their true temporal location (that
is, o_x at observation t holds the deviation that, strictly speaking,
belongs at t - 1). This is for compatibility with first differences: one
loses the first observation in each time series, not the last.

# outfile Printing

Variants:   outfile filename option
            outfile --close
Options:    --append (append to file)
            --write (overwrite file)
            --quiet (see below)
Examples:   outfile regress.txt --write
            outfile --close

Diverts output to filename, until further notice. Use the flag --append to
append output to an existing file or --write to start a new file (or
overwrite an existing one). Only one file can be opened in this way at any
given time.

The --close flag is used to close an output file that was previously opened
as above. Output will then revert to the default stream.

In the first example command above, the file regress.txt is opened for
writing, and in the second it is closed. This would make sense as a sequence
only if some commands were issued before the --close. For example if an
"ols" command intervened, its output would go to regress.txt rather than the
screen.

Three special variants on the above are available. If you give the keyword
null in place of a real filename along with the --write option, the effect
is to suppress all printed output until redirection is ended. If either of
the keywords stdout or stderr are given in place of a regular filename the
effect is to redirect output to standard output or standard error output
respectively.

The --quiet option is for use with --write or --append: its effect is to
turn off the echoing of commands and the printing of auxiliary messages
while output is redirected. It is equivalent to doing

	set echo off
	set messages off

except that when redirection is ended the original values of the echo and
messages variables are restored.

# panel Estimation

Arguments:  depvar indepvars 
Options:    --vcv (print covariance matrix)
            --fixed-effects (estimate with group fixed effects)
            --random-effects (random effects or GLS model)
            --nerlove (use the Nerlove transformation)
            --between (estimate the between-groups model)
            --robust (robust standard errors; see below)
            --time-dummies (include time dummy variables)
            --unit-weights (weighted least squares)
            --iterate (iterative estimation)
            --matrix-diff (use matrix-difference method for Hausman test)
            --quiet (less verbose output)
            --verbose (more verbose output)

Estimates a panel model. By default the fixed effects estimator is used;
this is implemented by subtracting the group or unit means from the original
data.

If the --random-effects flag is given, random effects estimates are
computed, by default using the method of Swamy and Arora (1972). In this
case (only) the option --matrix-diff forces use of the matrix-difference
method (as opposed to the regression method) for carrying out the Hausman
test for the consistency of the random effects estimator. Also specific to
the random effects estimator is the --nerlove flag, which selects the method
of Nerlove (1971) as opposed to Swamy and Arora.

Alternatively, if the --unit-weights flag is given, the model is estimated
via weighted least squares, with the weights based on the residual variance
for the respective cross-sectional units in the sample. In this case (only)
the --iterate flag may be added to produce iterative estimates: if the
iteration converges, the resulting estimates are Maximum Likelihood.

As a further alternative, if the --between flag is given, the between-groups
model is estimated (that is, an OLS regression using the group means).

The --robust option is available only for fixed effects models. The default
variant is the Arellano HAC estimator, but Beck-Katz "Panel Corrected
Standard Errors" can be selected via the command set pcse on.

For more details on panel estimation, please see the Gretl User's Guide.

Menu path:    /Model/Panel

# pca Statistics

Argument:   varlist 
Options:    --covariance (use the covariance matrix)
            --save[=n] (save major components)
            --save-all (save all components)
            --quiet (don't print results)

Principal Components Analysis. Unless the --quiet option is given, prints
the eigenvalues of the correlation matrix (or the covariance matrix if the
--covariance option is given) for the variables in varlist, along with the
proportion of the joint variance accounted for by each component. Also
prints the corresponding eigenvectors (or "component loadings").

If you give the --save-all option then all components are saved to the
dataset as series, with names PC1, PC2 and so on. These artificial variables
are formed as the sum of (component loading) times (standardized X_i), where
X_i denotes the ith variable in varlist.

If you give the --save option without a parameter value, components with
eigenvalues greater than the mean (which means greater than 1.0 if the
analysis is based on the correlation matrix) are saved to the dataset as
described above. If you provide a value for n with this option then the most
important n components are saved.

See also the "princomp" function.

Menu path:    /View/Principal components
Other access: Main window pop-up (multiple selection)

# pergm Statistics

Arguments:  series [ bandwidth ] 
Options:    --bartlett (use Bartlett lag window)
            --log (use log scale)
            --radians (show frequency in radians)
            --degrees (show frequency in degrees)
            --plot=mode-or-filename (see below)

Computes and displays the spectrum of the specified series. By default the
sample periodogram is given, but optionally a Bartlett lag window is used in
estimating the spectrum (see, for example, Greene's Econometric Analysis for
a discussion of this). The default width of the Bartlett window is twice the
square root of the sample size but this can be set manually using the
bandwidth parameter, up to a maximum of half the sample size.

If the --log option is given the spectrum is represented on a logarithmic
scale.

The (mutually exclusive) options --radians and --degrees influence the
appearance of the frequency axis when the periodogram is graphed. By default
the frequency is scaled by the number of periods in the sample, but these
options cause the axis to be labeled from 0 to pi radians or from 0 to
180degrees, respectively.

By default, if the program is not in batch mode a plot of the periodogram is
shown. This can be adjusted via the --plot option. The acceptable parameters
to this option are none (to suppress the plot); display (to display a plot
even when in batch mode); or a file name. The effect of providing a file
name is as described for the --output option of the "gnuplot" command.

Menu path:    /Variable/Periodogram
Other access: Main window pop-up menu (single selection)

# poisson Estimation

Arguments:  depvar indepvars [ ; offset ] 
Options:    --robust (robust standard errors)
            --cluster=clustvar (see "logit" for explanation)
            --vcv (print covariance matrix)
            --verbose (print details of iterations)
Examples:   poisson y 0 x1 x2
            poisson y 0 x1 x2 ; S

Estimates a poisson regression. The dependent variable is taken to represent
the occurrence of events of some sort, and must take on only non-negative
integer values.

If a discrete random variable Y follows the Poisson distribution, then

  Pr(Y = y) = exp(-v) * v^y / y!

for y = 0, 1, 2,.... The mean and variance of the distribution are both
equal to v. In the Poisson regression model, the parameter v is represented
as a function of one or more independent variables. The most common version
(and the only one supported by gretl) has

  v = exp(b0 + b1*x1 + b2*x2 + ...)

or in other words the log of v is a linear function of the independent
variables.

Optionally, you may add an "offset" variable to the specification. This is a
scale variable, the log of which is added to the linear regression function
(implicitly, with a coefficient of 1.0). This makes sense if you expect the
number of occurrences of the event in question to be proportional, other
things equal, to some known factor. For example, the number of traffic
accidents might be supposed to be proportional to traffic volume, other
things equal, and in that case traffic volume could be specified as an
"offset" in a Poisson model of the accident rate. The offset variable must
be strictly positive.

By default, standard errors are computed using the negative inverse of the
Hessian. If the --robust flag is given, then QML or Huber-White standard
errors are calculated instead. In this case the estimated covariance matrix
is a "sandwich" of the inverse of the estimated Hessian and the outer
product of the gradient.

See also "negbin".

Menu path:    /Model/Limited dependent variable/Count data...

# print Printing

Variants:   print varlist
            print
            print object_name
            print string_literal
Options:    --byobs (by observations)
            --no-dates (use simple observation numbers)
Examples:   print x1 x2 --byobs
            print my_matrix
            print "This is a string"

If varlist is given, prints the values of the specified series, or if no
argument is given, prints the values of all series in the current dataset.
If the --byobs flag is added the data are printed by observation, otherwise
they are printed by variable. When printing by observation, the default is
to show the date (with time-series data) or the observation marker string
(if any) at the start of each line. The --no-dates option suppresses the
printing of dates or markers; a simple observation number is shown instead.

Besides printing series, you may give the name of a (single) matrix or
scalar variable for printing. Or you may give a literal string argument,
enclosed in double quotes, to be printed as is. In these case the option
flags are not applicable.

Note that you can gain greater control over the printing format (and so, for
example, expose a greater number of digits than are shown by default) by
using "printf".

Menu path:    /Data/Display values

# printf Printing

Arguments:  format , args 

Prints scalar values, series, matrices, or strings under the control of a
format string (providing a subset of the printf() statement in the C
programming language). Recognized numeric formats are %e, %E, %f, %g, %G and
%d, in each case with the various modifiers available in C. Examples: the
format %.10g prints a value to 10 significant figures; %12.6f prints a value
to 6 decimal places, with a width of 12 characters. The format %s should be
used for strings.

The format string itself must be enclosed in double quotes. The values to be
printed must follow the format string, separated by commas. These values
should take the form of either (a) the names of variables, (b) expressions
that are valid for the "genr" command, or (c) the special functions
varname() or date(). The following example prints the values of two
variables plus that of a calculated expression:

	ols 1 0 2 3
	scalar b = $coeff[2]
	scalar se_b = $stderr[2]
	printf "b = %.8g, standard error %.8g, t = %.4f\n", 
          b, se_b, b/se_b

The next lines illustrate the use of the varname and date functions, which
respectively print the name of a variable, given its ID number, and a date
string, given a 1-based observation number.

	printf "The name of variable %d is %s\n", i, varname(i)
	printf "The date of observation %d is %s\n", j, date(j)

If a matrix argument is given in association with a numeric format, the
entire matrix is printed using the specified format for each element. The
same applies to series, except that the range of values printed is governed
by the current sample setting.

The maximum length of a format string is 127 characters. The escape
sequences \n (newline), \t (tab), \v (vertical tab) and \\ (literal
backslash) are recognized. To print a literal percent sign, use %%.

As in C, numerical values that form part of the format (width and or
precision) may be given directly as numbers, as in %10.4f, or they may be
given as variables. In the latter case, one puts asterisks into the format
string and supplies corresponding arguments in order. For example,

	scalar width = 12
	scalar precision = 6
	printf "x = %*.*f\n", width, precision, x

# probit Estimation

Arguments:  depvar indepvars 
Options:    --robust (robust standard errors)
            --cluster=clustvar (see "logit" for explanation)
            --vcv (print covariance matrix)
            --verbose (print details of iterations)
            --p-values (show p-values instead of slopes)
            --random-effects (estimates a random effects panel probit model)
            --quadpoints=k (number of quadrature points for RE estimation)

If the dependent variable is a binary variable (all values are 0 or 1)
maximum likelihood estimates of the coefficients on indepvars are obtained
via the Newton-Raphson method. As the model is nonlinear the slopes depend
on the values of the independent variables. By default the slopes with
respect to each of the independent variables are calculated (at the means of
those variables) and these slopes replace the usual p-values in the
regression output. This behavior can be suppressed my giving the --p-values
option. The chi-square statistic tests the null hypothesis that all
coefficients are zero apart from the constant.

By default, standard errors are computed using the negative inverse of the
Hessian. If the --robust flag is given, then QML or Huber-White standard
errors are calculated instead. In this case the estimated covariance matrix
is a "sandwich" of the inverse of the estimated Hessian and the outer
product of the gradient. See chapter 10 of Davidson and MacKinnon for
details.

If the dependent variable is not binary but is discrete, then Ordered Probit
estimates are obtained. (If the variable selected as dependent is not
discrete, an error is flagged.)

With the --random-effects option, the error term is assumed to be composed
of two normally distributed components: one time-invariant term that is
specific to the cross-sectional unit or "individual" (and is known as the
individual effect); and one term that is specific to the particular
observation.

Evaluation of the likelihood for this model involves the use of
Gauss-Hermite quadrature for approximating the value of expectations of
functions of normal variates. The number of quadrature points used can be
chosen through the --quadpoints option (the default is 32). Using more
points will increase the accuracy of the results, but at the cost of longer
compute time; with many quadrature points and a large dataset estimation may
be quite time consuming.

Besides the usual parameter estimates (and associated statistics) relating
to the included regressors, certain additional information is presented on
estimation of this sort of model:

  lnsigma2: the maximum likelihood estimate of the log of the variance of
  the individual effect;

  sigma_u: the estimated standard deviation of the individual effect; and

  rho: the estimated share of the individual effect in the composite error
  variance (also known as the intra-class correlation).

The Likelihood Ratio test of the null hypothesis that rho equals zero
provides a means of assessing whether the random effects specification is
needed. If the null is not rejected that suggests that a simple pooled
probit specification is adequate.

Probit for analysis of proportions is not implemented in gretl at this
point.

Menu path:    /Model/Limited dependent variable/Probit

# pvalue Utilities

Arguments:  dist [ params ] xval 
Examples:   pvalue z zscore
            pvalue t 25 3.0
            pvalue X 3 5.6
            pvalue F 4 58 fval
            pvalue G shape scale x
            pvalue B bprob 10 6
            pvalue P lambda x
            pvalue W shape scale x

Computes the area to the right of xval in the specified distribution (z for
Gaussian, t for Student's t, X for chi-square, F for F, G for gamma, B for
binomial, P for Poisson, or W for Weibull).

Depending on the distribution, the following information must be given,
before the xval: for the t and chi-square distributions, the degrees of
freedom; for F, the numerator and denominator degrees of freedom; for gamma,
the shape and scale parameters; for the binomial distribution, the "success"
probability and the number of trials; for the Poisson distribution, the
parameter lambda (which is both the mean and the variance); and for the
Weibull distribution, shape and scale parameters. As shown in the examples
above, the numerical parameters may be given in numeric form or as the names
of variables.

The parameters for the gamma distribution are sometimes given as mean and
variance rather than shape and scale. The mean is the product of the shape
and the scale; the variance is the product of the shape and the square of
the scale. So the scale may be found as the variance divided by the mean,
and the shape as the mean divided by the scale.

Menu path:    /Tools/P-value finder

# qlrtest Tests

For a model estimated on time-series data via OLS, performs the Quandt
likelihood ratio (QLR) test for a structural break at an unknown point in
time, with 15 percent trimming at the beginning and end of the sample
period.

For each potential break point within the central 70 percent of the
observations, a Chow test is performed. See "chow" for details; as with the
regular Chow test, this is a robust Wald test if the original model was
estimated with the --robust option, an F-test otherwise. The QLR statistic
is then the maximum of the individual test statistics.

An asymptotic p-value is obtained using the method of Bruce Hansen (1997).

Menu path:    Model window, /Tests/QLR test

# qqplot Graphs

Variants:   qqplot y
            qqplot y x
Options:    --z-scores (see below)
            --raw (see below)

Given just one series argument, displays a plot of the empirical quantiles
of the selected series (given by name or ID number) against the quantiles of
the normal distribution. The series must include at least 20 valid
observations in the current sample range. By default the empirical quantiles
are plotted against quantiles of the normal distribution having the same
mean and variance as the sample data, but two alternatives are available: if
the --z-scores option is given the data are standardized, while if the --raw
option is given the "raw" empirical quantiles are plotted against the
quantiles of the standard normal distribution.

Given two series arguments, y and x, displays a plot of the empirical
quantiles of y against those of x. The data values are not standardized.

Menu path:    /Variable/Normal Q-Q plot
Menu path:    /View/Graph specified vars/Q-Q plot

# quantreg Estimation

Arguments:  tau depvar indepvars 
Options:    --robust (robust standard errors)
            --intervals[=level] (compute confidence intervals)
            --vcv (print covariance matrix)
            --quiet (suppress printing of results)
Examples:   quantreg 0.25 y 0 xlist
            quantreg 0.5 y 0 xlist --intervals
            quantreg 0.5 y 0 xlist --intervals=.95
            quantreg tauvec y 0 xlist --robust
            See also mrw_qr.inp

Quantile regression. The first argument, tau, is the conditional quantile
for which estimates are wanted. It may be given either as a numerical value
or as the name of a pre-defined scalar variable; the value must be in the
range 0.01 to 0.99. (Alternatively, a vector of values may be given for tau;
see below for details.) The second and subsequent arguments compose a
regression list on the same pattern as "ols".

Without the --intervals option, standard errors are printed for the quantile
estimates. By default, these are computed according to the asymptotic
formula given by Koenker and Bassett (1978), but if the --robust option is
given, standard errors that are robust with respect to heteroskedasticity
are calculated using the method of Koenker and Zhao (1994).

When the --intervals option is chosen, confidence intervals are given for
the parameter estimates instead of standard errors. These intervals are
computed using the rank inversion method, and in general they are
asymmetrical about the point estimates. The specifics of the calculation are
inflected by the --robust option: without this, the intervals are computed
on the assumption of IID errors (Koenker, 1994); with it, they use the
robust estimator developed by Koenker and Machado (1999).

By default, 90 percent confidence intervals are produced. You can change
this by appending a confidence level (expressed as a decimal fraction) to
the intervals option, as in --intervals=0.95.

Vector-valued tau: instead of supplying a scalar, you may give the name of a
pre-defined matrix. In this case estimates are computed for all the given
tau values and the results are printed in a special format, showing the
sequence of quantile estimates for each regressor in turn.

Menu path:    /Model/Robust estimation/Quantile regression

# quit Utilities

Exits from the program, giving you the option of saving the output from the
session on the way out.

Menu path:    /File/Exit

# rename Dataset

Arguments:  series newname 

Changes the name of series (identified by name or ID number) to newname. The
new name must be of 31 characters maximum, must start with a letter, and
must be composed of only letters, digits, and the underscore character.

Menu path:    /Variable/Edit attributes
Other access: Main window pop-up menu (single selection)

# reset Tests

Options:    --quiet (don't print the auxiliary regression)
            --squares-only (compute the test using only the squares)
            --cubes-only (compute the test using only the cubes)

Must follow the estimation of a model via OLS. Carries out Ramsey's RESET
test for model specification (non-linearity) by adding the square and/or the
cube of the fitted values to the regression and calculating the F statistic
for the null hypothesis that the parameters on the added terms are zero.

Both the square and the cube are added, unless one of the options
--squares-only or --cubes-only is given.

Menu path:    Model window, /Tests/Ramsey's RESET

# restrict Tests

Options:    --quiet (don't print restricted estimates)
            --silent (don't print anything)
            --wald (system estimators only - see below)
            --bootstrap (bootstrap the test if possible)
            --full (OLS and VECMs only, see below)

Imposes a set of (usually linear) restrictions on either (a) the model last
estimated or (b) a system of equations previously defined and named. In all
cases the set of restrictions should be started with the keyword "restrict"
and terminated with "end restrict".

In the single equation case the restrictions are always implicitly to be
applied to the last model, and they are evaluated as soon as the restrict
block is closed.

In the case of a system of equations (defined via the "system" command), the
initial "restrict" may be followed by the name of a previously defined
system of equations. If this is omitted and the last model was a system then
the restrictions are applied to the last model. By default the restrictions
are evaluated when the system is next estimated, using the "estimate"
command. But if the --wald option is given the restriction is tested right
away, via a Wald chi-square test on the covariance matrix. Note that this
option will produce an error if a system has been defined but not yet
estimated.

Depending on the context, the restrictions to be tested may be expressed in
various ways. The simplest form is as follows: each restriction is given as
an equation, with a linear combination of parameters on the left and a
scalar value to the right of the equals sign (either a numerical constant or
the name of a scalar variable).

In the single-equation case, parameters may be referenced in the form b[i],
where i represents the position in the list of regressors (starting at 1),
or b[varname], where varname is the name of the regressor in question. In
the system case, parameters are referenced using b plus two numbers in
square brackets. The leading number represents the position of the equation
within the system and the second number indicates position in the list of
regressors. For example b[2,1] denotes the first parameter in the second
equation, and b[3,2] the second parameter in the third equation. The b terms
in the equation representing a restriction may be prefixed with a numeric
multiplier, for example 3.5*b[4].

Here is an example of a set of restrictions for a previously estimated
model:

	restrict
	 b[1] = 0
	 b[2] - b[3] = 0
	 b[4] + 2*b[5] = 1
	end restrict

And here is an example of a set of restrictions to be applied to a named
system. (If the name of the system does not contain spaces, the surrounding
quotes are not required.)

	restrict "System 1"
	 b[1,1] = 0
	 b[1,2] - b[2,2] = 0
	 b[3,4] + 2*b[3,5] = 1
	end restrict

In the single-equation case the restrictions are by default evaluated via a
Wald test, using the covariance matrix of the model in question. If the
original model was estimated via OLS then the restricted coefficient
estimates are printed; to suppress this, append the --quiet option flag to
the initial restrict command. As an alternative to the Wald test, for models
estimated via OLS or WLS only, you can give the --bootstrap option to
perform a bootstrapped test of the restriction.

In the system case, the test statistic depends on the estimator chosen: a
Likelihood Ratio test if the system is estimated using a Maximum Likelihood
method, or an asymptotic F-test otherwise.

There are two alternatives to the method of expressing restrictions
discussed above. First, a set of g linear restrictions on a k-vector of
parameters, beta, may be written compactly as Rbeta - q = 0, where R is an g
x k matrix and q is a g-vector. You can specify a restriction by giving the
names of pre-defined, conformable matrices to be used as R and q, as in

	restrict 
	  R = Rmat
	  q = qvec
	end restrict

Secondly, if you wish to test a nonlinear restriction (this is currently
available for single-equation models only) you should give the restriction
as the name of a function, preceded by "rfunc = ", as in

	restrict
	  rfunc = myfunction
	end restrict

The constraint function should take a single const matrix argument; this
will be automatically filled out with the parameter vector. And it should
return a vector which is zero under the null hypothesis, non-zero otherwise.
The length of the vector is the number of restrictions. This function is
used as a "callback" by gretl's numerical Jacobian routine, which calculates
a Wald test statistic via the delta method.

Here is a simple example of a function suitable for testing one nonlinear
restriction, namely that two pairs of parameter values have a common ratio.

	function matrix restr (const matrix b)
	  matrix v = b[1]/b[2] - b[4]/b[5]
	  return v
	end function

On successful completion of the restrict command the accessors $test and
$pvalue give the test statistic and its p-value.

When testing restrictions on a single-equation model estimated via OLS, or
on a VECM, the --full option can be used to set the restricted estimates as
the "last model" for the purposes of further testing or the use of accessors
such as $coeff and $vcv. Note that some special considerations apply in the
case of testing restrictions on Vector Error Correction Models. Please see
the Gretl User's Guide for details.

Menu path:    Model window, /Tests/Linear restrictions

# rmplot Graphs

Argument:   series 
Options:    --trim (see below)
            --quiet (suppress printed output)

Range-mean plot: this command creates a simple graph to help in deciding
whether a time series, y(t), has constant variance or not. We take the full
sample t=1,...,T and divide it into small subsamples of arbitrary size k.
The first subsample is formed by y(1),...,y(k), the second is y(k+1), ...,
y(2k), and so on. For each subsample we calculate the sample mean and range
(= maximum minus minimum), and we construct a graph with the means on the
horizontal axis and the ranges on the vertical. So each subsample is
represented by a point in this plane. If the variance of the series is
constant we would expect the subsample range to be independent of the
subsample mean; if we see the points approximate an upward-sloping line this
suggests the variance of the series is increasing in its mean; and if the
points approximate a downward sloping line this suggests the variance is
decreasing in the mean.

Besides the graph, gretl displays the means and ranges for each subsample,
along with the slope coefficient for an OLS regression of the range on the
mean and the p-value for the null hypothesis that this slope is zero. If the
slope coefficient is significant at the 10 percent significance level then
the fitted line from the regression of range on mean is shown on the graph.
The t-statistic for the null, and the corresponding p-value, are recorded
and may be retrieved using the accessors $test and $pvalue respectively.

If the --trim option is given, the minimum and maximum values in each
sub-sample are discarded before calculating the mean and range. This makes
it less likely that outliers will distort the analysis.

If the --quiet option is given, no graph is shown and no output is printed;
only the t-statistic and p-value are recorded.

Menu path:    /Variable/Range-mean graph

# run Programming

Argument:   filename 

Executes the commands in filename then returns control to the interactive
prompt. This command is intended for use with the command-line program
gretlcli, or at the "gretl console" in the GUI program.

See also "include".

Menu path:    Run icon in script window

# runs Tests

Argument:   series 
Options:    --difference (use first difference of variable)
            --equal (positive and negative values are equiprobable)

Carries out the nonparametric "runs" test for randomness of the specified
series, where runs are defined as sequences of consecutive positive or
negative values. If you want to test for randomness of deviations from the
median, for a variable named x1 with a non-zero median, you can do the
following:

	series signx1 = x1 - median(x1)
	runs signx1

If the --difference option is given, the variable is differenced prior to
the analysis, hence the runs are interpreted as sequences of consecutive
increases or decreases in the value of the variable.

If the --equal option is given, the null hypothesis incorporates the
assumption that positive and negative values are equiprobable, otherwise the
test statistic is invariant with respect to the "fairness" of the process
generating the sequence, and the test focuses on independence alone.

Menu path:    /Tools/Nonparametric tests

# scatters Graphs

Arguments:  yvar ; xvars  or yvars ; xvar 
Options:    --with-lines (create line graphs)
            --matrix=name (plot columns of named matrix)
            --output=filename (send output to specified file)
            --output=filename (send output to specified file)
Examples:   scatters 1 ; 2 3 4 5
            scatters 1 2 3 4 5 6 ; 7
            scatters y1 y2 y3 ; x --with-lines

Generates pairwise graphs of yvar against all the variables in xvars, or of
all the variables in yvars against xvar. The first example above puts
variable 1 on the y-axis and draws four graphs, the first having variable 2
on the x-axis, the second variable 3 on the x-axis, and so on. The second
example plots each of variables 1 through 6 against variable 7 on the
x-axis. Scanning a set of such plots can be a useful step in exploratory
data analysis. The maximum number of plots is 16; any extra variable in the
list will be ignored.

By default the graphs are scatterplots, but if you give the --with-lines
flag they will be line graphs.

For details on usage of the --output option, please see the "gnuplot"
command.

If a named matrix is specified as the data source the x and y lists should
be given as 1-based column numbers; or alternatively, if no such numbers are
given, all the columns are plotted against time or an index variable.

If the dataset is time-series, then the second sub-list can be omitted, in
which case it will implicitly be taken as "time", so you can plot multiple
time series in separated sub-graphs

Menu path:    /View/Multiple graphs

# sdiff Transformations

Argument:   varlist 

The seasonal difference of each variable in varlist is obtained and the
result stored in a new variable with the prefix sd_. This command is
available only for seasonal time series.

Menu path:    /Add/Seasonal differences of selected variables

# set Programming

Variants:   set variable value
            set --to-file=filename
            set --from-file=filename
            set stopwatch
            set
Examples:   set svd on
            set csv_delim tab
            set horizon 10
            set --to-file=mysettings.inp

The most common use of this command is the first variant shown above, where
it is used to set the value of a selected program parameter. This is
discussed in detail below. The other uses are: with --to-file, to write a
script file containing all the current parameter settings; with --from-file
to read a script file containing parameter settings and apply them to the
current session; with stopwatch to zero the gretl "stopwatch" which can be
used to measure CPU time (see the entry for the $stopwatch accessor in the
gretl function reference); or, if the word set is given alone, to print the
current settings.

Values set via this comand remain in force for the duration of the gretl
session unless they are changed by a further call to "set". The parameters
that can be set in this way are enumerated below. Note that the settings of
hc_version, hac_lag and hac_kernel are used when the --robust option is
given to an estimation command.

The available settings are grouped under the following categories: program
interaction and behavior, numerical methods, random number generation,
robust estimation, filtering, time series estimation, and interaction with
GNU R.

Program interaction and behavior

These settings are used for controlling various aspects of the way gretl
interacts with the user.

  csv_delim: either comma (the default), space, tab or semicolon. Sets the
  column delimiter used when saving data to file in CSV format.

  csv_write_na: the string used to represent missing values when writing
  data to file in CSV format. Maximum 7 characters; the default is NA.

  csv_read_na: the string taken to represent missing values (NAs) when
  reading data in CSV format. Maximum 7 characters. The default depends on
  whether a data column is found to contain numerical data (mostly) or
  string values. For numerical data the following are taken as indicating
  NAs: an empty cell, or any of the strings NA, N.A., na, n.a., N/A, #N/A,
  NaN, .NaN, ., .., -999, and -9999. For string-valued data only a blank
  cell, or a cell containing an empty string, is counted as NA. These
  defaults can be reimposed by giving default as the value for csv_read_na.
  To specify that only empty cells are read as NAs, give a value of "". Note
  that empty cells are always read as NAs regardless of the setting of this
  variable.

  csv_digits: a positive integer specifying the number of significant digits
  to use when writing data in CSV format. By default up to 12 digits are
  used depending on the precision of the original data. Note that CSV output
  employs the C library's fprintf function with "%g" conversion, which means
  that trailing zeros are dropped.

  echo: off or on (the default). Suppress or resume the echoing of commands
  in gretl's output.

  force_decpoint: on or off (the default). Force gretl to use the decimal
  point character, in a locale where another character (most likely the
  comma) is the standard decimal separator.

  halt_on_error: off or on (the default). By default, when an error is
  encountered in the course of executing a script, execution is halted (and
  if the command-line program is operating in batch mode, it exits with a
  non-zero return status). You can force gretl to continue on error by
  setting halt_on_error to off (or by setting the environment variable
  GRETL_KEEP_GOING to 1). If an error occurs while "compiling" a loop or
  user-defined function, however, execution is halted regardless.

  loop_maxiter: one non-negative integer value (default 100000). Sets the
  maximum number of iterations that a while loop is allowed before halting
  (see "loop"). Note that this setting only affects the while variant; its
  purpose is to guard against inadvertently infinite loops. Setting this
  value to 0 has the effect of disabling the limit; use with caution.

  max_verbose: on or off (the default). Toggles verbose output for the
  BFGSmax and NRmax functions (see the User's Guide for details).

  messages: off or on (the default). Suppress or resume the printing of
  non-error messages associated with various commands, for example when a
  new variable is generated or when the sample range is changed.

  warnings: off or on (the default). Suppress or resume the printing of
  warning messages issued when arithmetical operations produce non-finite
  values.

  debug: 1, 2 or 0 (the default). This is for use with user-defined
  functions. Setting debug to 1 is equivalent to turning messages on within
  all such functions; setting this variable to 2 has the additional effect
  of turning on max_verbose within all functions.

  shell_ok: on or off (the default). Enable launching external programs from
  gretl via the system shell. This is disabled by default for security
  reasons, and can only be enabled via the graphical user interface
  (Tools/Preferences/General). However, once set to on, this setting will
  remain active for future sessions until explicitly disabled.

  shelldir: path. Sets the current working directory for shell commands.

  use_cwd: on or off (the default). This setting affects the behavior of the
  "outfile" and "store" commands, which write external files. Normally, the
  file will be written in the user's default data directory; if use_cwd is
  on, on the contrary, the file will be created in the working directory
  when gretl was started.

  bfgs_verbskip: one integer. This setting affects the behavior of the
  --verbose option to those commands that use BFGS as an optimization
  algorithm and is used to compact output. if bfgs_verbskip is set to, say,
  3, then the --verbose switch will only print iterations 3, 6, 9 and so on.

  skip_missing: on (the default) or off. Controls gretl's behavior when
  contructing a matrix from data series: the default is to skip data rows
  that contain one or more missing values but if skip_missing is set off
  missing values are converted to NaNs.

  matrix_mask: the name of a series, or the keyword null. Offers greater
  control than skip_missing when constructing matrices from series: the data
  rows selected for matrices are those with non-zero (and non-missing)
  values in the specified series. The selected mask remains in force until
  it is replaced, or removed via the null keyword.

  huge: a large positive number (by default, 1.0E100). This setting controls
  the value returned by the accessor "$huge".

Numerical methods

These settings are used for controlling the numerical algorithms that gretl
uses for estimation.

  optimizer: either auto (the default), BFGS or newton. Sets the
  optimization algorithm used for various ML estimators, in cases where both
  BFGS and Newton-Raphson are applicable. The default is to use
  Newton-Raphson where an analytical Hessian is available, otherwise BFGS.

  bhhh_maxiter: one integer, the maximum number of iterations for gretl's
  internal BHHH routine, which is used in the "arma" command for conditional
  ML estimation. If convergence is not achieved after bhhh_maxiter, the
  program returns an error. The default is set at 500.

  bhhh_toler: one floating point value, or the string default. This is used
  in gretl's internal BHHH routine to check if convergence has occurred. The
  algorithm stops iterating as soon as the increment in the log-likelihood
  between iterations is smaller than bhhh_toler. The default value is
  1.0E-06; this value may be re-established by typing default in place of a
  numeric value.

  bfgs_maxiter: one integer, the maximum number of iterations for gretl's
  BFGS routine, which is used for "mle", "gmm" and several specific
  estimators. If convergence is not achieved in the specified number of
  iterations, the program returns an error. The default value depends on the
  context, but is typically of the order of 500.

  bfgs_toler: one floating point value, or the string default. This is used
  in gretl's BFGS routine to check if convergence has occurred. The
  algorithm stops as soon as the relative improvement in the objective
  function between iterations is smaller than bfgs_toler. The default value
  is the machine precision to the power 3/4; this value may be
  re-established by typing default in place of a numeric value.

  bfgs_maxgrad: one floating point value. This is used in gretl's BFGS
  routine to check if the norm of the gradient is reasonably close to zero
  when the bfgs_toler criterion is met. A warning is printed if the norm of
  the gradient exceeds 1; an error is flagged if the norm exceeds
  bfgs_maxgrad. At present the default is the permissive value of 5.0.

  bfgs_richardson: on or off (the default). Use Richardson extrapolation
  when computing numerical derivatives in the context of BFGS maximization.

  initvals: either auto (the default) or the name of a pre-specified matrix.
  Allows manual setting of the initial parameter estimates for numerical
  optimization problems (such as ARMA estimation). For details see the Gretl
  User's Guide.

  lbfgs: on or off (the default). Use the limited-memory version of BFGS
  (L-BFGS-B) instead of the ordinary algorithm. This may be advantageous
  when the function to be maximized is not globally concave.

  lbfgs_mem: an integer value in the range 3 to 20 (with a default value of
  8). This determines the number of corrections used in the limited memory
  matrix when L-BFGS-B is employed.

  nls_toler: a floating-point value (the default is the machine precision to
  the power 3/4). Sets the tolerance used in judging whether or not
  convergence has occurred in nonlinear least squares estimation using the
  "nls" command.

  svd: on or off (the default). Use SVD rather than Cholesky or QR
  decomposition in least squares calculations. This option applies to the
  mols function as well as various internal calculations, but not to the
  regular "ols" command.

  fcp: on or off (the default). Use the algorithm of Fiorentini, Calzolari
  and Panattoni rather than native gretl code when computing GARCH
  estimates.

  gmm_maxiter: one integer, the maximum number of iterations for gretl's gmm
  command when in iterated mode (as opposed to one- or two-step). The
  default value is 250.

  nadarwat_trim: one integer, the trim parameter used in the nadarwat
  function.

Random number generation

  seed: an unsigned integer. Sets the seed for the pseudo-random number
  generator. By default this is set from the system time; if you want to
  generate repeatable sequences of random numbers you must set the seed
  manually.

  RNG: either MT or SFMT (the default). Switches between the default random
  number generator, namely the SIMD-oriented Fast Mersenne Twister (SFMT),
  and the Mersenne Twister of 2002 as implemented in GLib (MT). SFMT is
  faster and has better distributional properties but MT was gretl's RNG up
  to version 1.9.3.

  normal_rand: ziggurat (the default) or box-muller. Sets the method for
  generating random normal samples based on uniform input.

Robust estimation

  bootrep: an integer. Sets the number of replications for the "restrict"
  command with the --bootstrap option.

  garch_vcv: unset, hessian, im (information matrix) , op (outer product
  matrix), qml (QML estimator), bw (Bollerslev-Wooldridge). Specifies the
  variant that will be used for estimating the coefficient covariance
  matrix, for GARCH models. If unset is given (the default) then the Hessian
  is used unless the "robust" option is given for the garch command, in
  which case QML is used.

  arma_vcv: hessian (the default) or op (outer product matrix). Specifies
  the variant to be used when computing the covariance matrix for ARIMA
  models.

  force_hc: off (the default) or on. By default, with time-series data and
  when the --robust option is given with ols, the HAC estimator is used. If
  you set force_hc to "on", this forces calculation of the regular
  Heteroskedasticity Consistent Covariance Matrix (HCCM), which does not
  take autocorrelation into account. Note that VARs are treated as a special
  case: when the --robust option is given the default method is regular
  HCCM, but the --robust-hac flag can be used to force the use of a HAC
  estimator.

  hac_lag: nw1 (the default), nw2, nw3 or an integer. Sets the maximum lag
  value or bandwidth, p, used when calculating HAC (Heteroskedasticity and
  Autocorrelation Consistent) standard errors using the Newey-West approach,
  for time series data. nw1 and nw2 represent two variant automatic
  calculations based on the sample size, T: for nw1, p = 0.75 * T^(1/3), and
  for nw2, p = 4 * (T/100)^(2/9). nw3 calls for data-based bandwidth
  selection. See also qs_bandwidth and hac_prewhiten below.

  hac_kernel: bartlett (the default), parzen, or qs (Quadratic Spectral).
  Sets the kernel, or pattern of weights, used when calculating HAC standard
  errors.

  hac_prewhiten: on or off (the default). Use Andrews-Monahan prewhitening
  and re-coloring when computing HAC standard errors. This also implies use
  of data-based bandwidth selection.

  hc_version: 0 (the default), 1, 2, 3 or 3a. Sets the variant used when
  calculating Heteroskedasticity Consistent standard errors with
  cross-sectional data. The first four options correspond to the HC0, HC1,
  HC2 and HC3 discussed by Davidson and MacKinnon in Econometric Theory and
  Methods, chapter 5. HC0 produces what are usually called "White's standard
  errors". Variant 3a is the MacKinnon-White "jackknife" procedure.

  pcse: off (the default) or on. By default, when estimating a model using
  pooled OLS on panel data with the --robust option, the Arellano estimator
  is used for the covariance matrix. If you set pcse to "on", this forces
  use of the Beck and Katz Panel Corrected Standard Errors (which do not
  take autocorrelation into account).

  qs_bandwidth: Bandwidth for HAC estimation in the case where the Quadratic
  Spectral kernel is selected. (Unlike the Bartlett and Parzen kernels, the
  QS bandwidth need not be an integer.)

Time series

  horizon: one integer (the default is based on the frequency of the data).
  Sets the horizon for impulse responses and forecast variance
  decompositions in the context of vector autoregressions.

  vecm_norm: phillips (the default), diag, first or none. Used in the
  context of VECM estimation via the "vecm" command for identifying the
  cointegration vectors. See the the Gretl User's Guide for details.

Interaction with R

  R_lib: on (the default) or off. When sending instructions to be executed
  by R, use the R shared library by preference to the R executable, if the
  library is available.

  R_functions: off (the default) or on. Recognize functions defined in R as
  if they were native functions (the namespace prefix "R." is required). See
  the Gretl User's Guide for details on this and the previous item.

# setinfo Dataset

Argument:   series 
Options:    --description=string (set description)
            --graph-name=string (set graph name)
            --discrete (mark series as discrete)
            --continuous (mark series as continuous)
Examples:   setinfo x1 --description="Description of x1"
            setinfo y --graph-name="Some string"
            setinfo z --discrete

Sets up to three attributes of series, given by name or ID number, as
follows.

If the --description flag is given followed by a string in double quotes,
that string is used to set the variable's descriptive label. This label is
shown in response to the "labels" command, and is also shown in the main
window of the GUI program.

If the --graph-name flag is given followed by a quoted string, that string
will be used in place of the variable's name in graphs.

If one or other of the --discrete or --continuous option flags is given, the
variable's numerical character is set accordingly. The default is to treat
all series as continuous; setting a series as discrete affects the way the
variable is handled in frequency plots.

Menu path:    /Variable/Edit attributes
Other access: Main window pop-up menu

# setobs Dataset

Variants:   setobs periodicity startobs
            setobs unitvar timevar --panel-vars
Options:    --cross-section (interpret as cross section)
            --time-series (interpret as time series)
            --stacked-cross-section (interpret as panel data)
            --stacked-time-series (interpret as panel data)
            --panel-vars (use index variables, see below)
            --panel-time (see below)
            --panel-groups (see below)
Examples:   setobs 4 1990:1 --time-series
            setobs 12 1978:03
            setobs 1 1 --cross-section
            setobs 20 1:1 --stacked-time-series
            setobs unit year --panel-vars

This command forces the program to interpret the current data set as having
a specified structure.

In the first form of the command the periodicity, which must be an integer,
represents frequency in the case of time-series data (1 = annual; 4 =
quarterly; 12 = monthly; 52 = weekly; 5, 6, or 7 = daily; 24 = hourly). In
the case of panel data the periodicity means the number of lines per data
block: this corresponds to the number of cross-sectional units in the case
of stacked cross-sections, or the number of time periods in the case of
stacked time series. In the case of simple cross-sectional data the
periodicity should be set to 1.

The starting observation represents the starting date in the case of time
series data. Years may be given with two or four digits; subperiods (for
example, quarters or months) should be separated from the year with a colon.
In the case of panel data the starting observation should be given as 1:1;
and in the case of cross-sectional data, as 1. Starting observations for
daily or weekly data should be given in the form YYYY-MM-DD (or simply as 1
for undated data).

If no explicit option flag is given to indicate the structure of the data
the program will attempt to guess the structure from the information given.

The second form of the command (which requires the --panel-vars flag) may be
used to impose a panel interpretation when the data set contains variables
that uniquely identify the cross-sectional units and the time periods. The
data set will be sorted as stacked time series, by ascending values of the
units variable, unitvar.

Panel-specific options

The --panel-time and --panel-groups options can only be used with a dataset
which has already been defined as a panel.

The purpose of --panel-time is to set extra information regarding the time
dimension of the panel. This should be given on the pattern of the first
form of setobs noted above. For example, the following may be used to
indicate that the time dimension of a panel is quarterly, starting in the
first quarter of 1990.

	setobs 4 1990:1 --panel-time

The purpose of --panel-groups is to create a string-valued series holding
names for the groups (individuals, cross-sectional units) in the panel. With
this option you must supply a name for the series and a string variable
holding a list of group names (in that order). The names should be separated
by spaces; if a name includes spaces it should be wrapped in
backslash-escaped double-quotes. For example, the following will create a
series named country in which the names in cstrs are each repeated T times,
T being the time-series length of the panel.

	string cstrs
	sprintf cstrs "France Germany Italy \"United Kingdom\""
	setobs country cstrs --panel-groups

Menu path:    /Data/Dataset structure

# setmiss Dataset

Arguments:  value [ varlist ] 
Examples:   setmiss -1
            setmiss 100 x2

Get the program to interpret some specific numerical data value (the first
parameter to the command) as a code for "missing", in the case of imported
data. If this value is the only parameter, as in the first example above,
the interpretation will be applied to all series in the data set. If "value"
is followed by a list of variables, by name or number, the interpretation is
confined to the specified variable(s). Thus in the second example the data
value 100 is interpreted as a code for "missing", but only for the variable
x2.

Menu path:    /Data/Set missing value code

# shell Utilities

Argument:   shellcommand 
Examples:   ! ls -al
            ! notepad
            launch notepad

A "!", or the keyword "launch", at the beginning of a command line is
interpreted as an escape to the user's shell. Thus arbitrary shell commands
can be executed from within gretl. When "!" is used, the external command is
executed synchronously. That is, gretl waits for it to complete before
proceeding. If you want to start another program from within gretl and not
wait for its completion (asynchronous operation), use "launch" instead.

For reasons of security this facility is not enabled by default. To activate
it, check the box titled "Allow shell commands" under the File, Preferences
menu in the GUI program. This also makes shell commands available in the
command-line program (and is the only way to do so).

# smpl Dataset

Variants:   smpl startobs endobs
            smpl +i -j
            smpl dumvar --dummy
            smpl condition --restrict
            smpl --no-missing [ varlist ]
            smpl --contiguous [ varlist ]
            smpl n --random
            smpl full
Options:    --dummy (argument is a dummy variable)
            --restrict (apply boolean restriction)
            --replace (replace any existing boolean restriction)
            --no-missing (restrict to valid observations)
            --contiguous (see below)
            --random (form random sub-sample)
            --balanced (panel data: try to retain balanced panel)
Examples:   smpl 3 10
            smpl 1960:2 1982:4
            smpl +1 -1
            smpl x > 3000 --restrict
            smpl y > 3000 --restrict --replace
            smpl 100 --random

Resets the sample range. The new range can be defined in several ways. In
the first alternate form (and the first two examples) above, startobs and
endobs must be consistent with the periodicity of the data. Either one may
be replaced by a semicolon to leave the value unchanged. In the second form,
the integers i and j (which may be positive or negative, and should be
signed) are taken as offsets relative to the existing sample range. In the
third form dummyvar must be an indicator variable with values 0 or 1 at each
observation; the sample will be restricted to observations where the value
is 1. The fourth form, using --restrict, restricts the sample to
observations that satisfy the given Boolean condition (which is specified
according to the syntax of the "genr" command).

With the --no-missing form, if varlist is specified observations are
selected on condition that all variables in varlist have valid values at
that observation; otherwise, if no varlist is given, observations are
selected on condition that all variables have valid (non-missing) values.

The --contiguous form of smpl is intended for use with time series data. The
effect is to trim any observations at the start and end of the current
sample range that contain missing values (either for the variables in
varlist, or for all data series if no varlist is given). Then a check is
performed to see if there are any missing values in the remaining range; if
so, an error is flagged.

With the --random flag, the specified number of cases are selected from the
current dataset at random (without replacement). If you wish to be able to
replicate this selection you should set the seed for the random number
generator first (see the "set" command).

The final form, smpl full, restores the full data range.

Note that sample restrictions are, by default, cumulative: the baseline for
any smpl command is the current sample. If you wish the command to act so as
to replace any existing restriction you can add the option flag --replace to
the end of the command. (But this option is not compatible with the
--contiguous option.)

The internal variable obs may be used with the --restrict form of smpl to
exclude particular observations from the sample. For example

	smpl obs!=4 --restrict

will drop just the fourth observation. If the data points are identified by
labels,

	smpl obs!="USA" --restrict

will drop the observation with label "USA".

One point should be noted about the --dummy, --restrict and --no-missing
forms of smpl: "structural" information in the data file (regarding the time
series or panel nature of the data) is likely to be lost when this command
is issued. You may reimpose structure with the "setobs" command. A related
option, for use with panel data, is the --balanced flag: this requests that
a balanced panel is reconstituted after sub-sampling, via the insertion of
"missing rows" if need be. But note that it is not always possible to comply
with this request.

Please see the Gretl User's Guide for further details.

Menu path:    /Sample

# spearman Statistics

Arguments:  series1 series2 
Option:     --verbose (print ranked data)

Prints Spearman's rank correlation coefficient for the series series1 and
series2. The variables do not have to be ranked manually in advance; the
function takes care of this.

The automatic ranking is from largest to smallest (i.e. the largest data
value gets rank 1). If you need to invert this ranking, create a new
variable which is the negative of the original. For example:

	series altx = -x
	spearman altx y

Menu path:    /Model/Robust estimation/Rank correlation

# sprintf Printing

Arguments:  stringvar format , args 

This command works exactly like the "printf" command, printing the given
arguments under the control of the format string, except that the result is
written into the named string, stringvar.

# square Transformations

Argument:   varlist 
Option:     --cross (generate cross-products as well as squares)

Generates new series which are squares of the series in varlist (plus
cross-products if the --cross option is given). For example, "square x y"
will generate sq_x = x squared, sq_y = y squared and (optionally) x_y = x
times y. If a particular variable is a dummy variable it is not squared
because we will get the same variable.

Menu path:    /Add/Squares of selected variables

# store Dataset

Arguments:  filename [ varlist ] 
Options:    --csv (use CSV format)
            --omit-obs (see below, on CSV format)
            --no-header (see below, on CSV format)
            --gnu-octave (use GNU Octave format)
            --gnu-R (use GNU R format)
            --traditional (use traditional ESL format)
            --gzipped (apply gzip compression)
            --jmulti (use JMulti ASCII format)
            --dat (use PcGive ASCII format)
            --decimal-comma (use comma as decimal character)
            --database (use gretl database format)
            --overwrite (see below, on database format)
            --comment=string (see below)

Save data to filename. By default all currently defined series are saved but
the optional varlist argument can be used to select a subset of series. If
the dataset is sub-sampled, only the observations in the current sample
range are saved.

By default the data are saved in "native" gretl format, but the option flags
permit saving in several alternative formats. CSV (Comma-Separated Values)
data may be read into spreadsheet programs, and can also be manipulated
using a text editor. The formats of Octave, R and PcGive are designed for
use with the respective programs. Gzip compression may be useful for large
datasets. See the Gretl User's Guide for details on the various formats.

The option flags --omit-obs and --no-header are applicable only when saving
data in CSV format. By default, if the data are time series or panel, or if
the dataset includes specific observation markers, the CSV file includes a
first column identifying the observations (e.g. by date). If the --omit-obs
flag is given this column is omitted. The --no-header flag suppresses the
usual printing of the names of the variables at the top of the columns.

The option flag --decimal-comma is also confined to the case of saving data
in CSV format. The effect of this option is to replace the decimal point
with the decimal comma; in addition the column separator is forced to be a
semicolon.

The option of saving in gretl database format is intended to help with the
construction of large sets of series, possibly having mixed frequencies and
ranges of observations. At present this option is available only for annual,
quarterly or monthly time-series data. If you save to a file that already
exists, the default action is to append the newly saved series to the
existing content of the database. In this context it is an error if one or
more of the variables to be saved has the same name as a variable that is
already present in the database. The --overwrite flag has the effect that,
if there are variable names in common, the newly saved variable replaces the
variable of the same name in the original dataset.

The --comment option is available when saving data as a database or in CSV
format. The required parameter is a double-quoted one-line string, attached
to the option flag with an equals sign. The string is inserted as a comment
into the database index file or at the top of the CSV output.

The store command behaves in a special manner in the context of a
"progressive loop". See the Gretl User's Guide for details.

Menu path:    /File/Save data; /File/Export data

# summary Statistics

Variants:   summary [ varlist ]
            summary --matrix=matname
Options:    --simple (basic statistics only)
            --by=byvar (see below)

In its first form, this command prints summary statistics for the variables
in varlist, or for all the variables in the data set if varlist is omitted.
By default, output consists of the mean, standard deviation (sd),
coefficient of variation (= sd/mean), median, minimum, maximum, skewness
coefficient, and excess kurtosis. If the --simple option is given, output is
restricted to the mean, minimum, maximum and standard deviation.

If the --by option is given (in which case the parameter byvar should be the
name of a discrete variable), then statistics are printed for sub-samples
corresponding to the distinct values taken on by byvar. For example, if
byvar is a (binary) dummy variable, statistics are given for the cases byvar
= 0 and byvar = 1.

If the alternative form is given, using a named matrix, then summary
statistics are printed for each column of the matrix. The --by option is not
available in this case.

Menu path:    /View/Summary statistics
Other access: Main window pop-up menu

# system Estimation

Variants:   system method=estimator
            sysname <- system
Examples:   "Klein Model 1" <- system
            system method=sur
            system method=3sls
            See also klein.inp, kmenta.inp, greene14_2.inp

Starts a system of equations. Either of two forms of the command may be
given, depending on whether you wish to save the system for estimation in
more than one way or just estimate the system once.

To save the system you should assign it a name, as in the first example (if
the name contains spaces it must be surrounded by double quotes). In this
case you estimate the system using the "estimate" command. With a saved
system of equations, you are able to impose restrictions (including
cross-equation restrictions) using the "restrict" command.

Alternatively you can specify an estimator for the system using method=
followed by a string identifying one of the supported estimators: "ols"
(Ordinary Least Squares), "tsls" (Two-Stage Least Squares) "sur" (Seemingly
Unrelated Regressions), "3sls" (Three-Stage Least Squares), "fiml" (Full
Information Maximum Likelihood) or "liml" (Limited Information Maximum
Likelihood). In this case the system is estimated once its definition is
complete.

An equation system is terminated by the line "end system". Within the system
four sorts of statement may be given, as follows.

  "equation": specify an equation within the system. At least two such
  statements must be provided.

  "instr": for a system to be estimated via Three-Stage Least Squares, a
  list of instruments (by variable name or number). Alternatively, you can
  put this information into the "equation" line using the same syntax as in
  the "tsls" command.

  "endog": for a system of simultaneous equations, a list of endogenous
  variables. This is primarily intended for use with FIML estimation, but
  with Three-Stage Least Squares this approach may be used instead of giving
  an "instr" list; then all the variables not identified as endogenous will
  be used as instruments.

  "identity": for use with FIML, an identity linking two or more of the
  variables in the system. This sort of statement is ignored when an
  estimator other than FIML is used.

After estimation using the "system" or "estimate" commands the following
accessors can be used to retrieve additional information:

  $uhat: the matrix of residuals, one column per equation.

  $yhat: matrix of fitted values, one column per equation.

  $coeff: column vector of coefficients (all the coefficients from the first
  equation, followed by those from the second equation, and so on).

  $vcv: covariance matrix of the coefficients. If there are k elements in
  the $coeff vector, this matrix is k by k.

  $sigma: cross-equation residual covariance matrix.

  $sysGamma, $sysA and $sysB: structural-form coefficient matrices (see
  below).

If you want to retrieve the residuals or fitted values for a specific
equation as a data series, select a column from the $uhat or $yhat matrix
and assign it to a series, as in

	series uh1 = $uhat[,1]

The structural-form matrices correspond to the following representation of a
simultaneous equations model:

  Gamma y(t) = A y(t-1) + B x(t) + e(t)

If there are n endogenous variables and k exogenous variables, Gamma is an n
x n matrix and B is n x k. If the system contains no lags of the endogenous
variables then the A matrix is not present. If the maximum lag of an
endogenous regressor is p, the A matrix is n x np.

Menu path:    /Model/Simultaneous equations

# tabprint Printing

Argument:   [ -f filename ] 
Options:    --rtf (Produce RTF instead of LaTeX)
            --complete (Create a complete document)
            --format="f1|f2|f3|f4" (Specify a custom format)

Must follow the estimation of a model. Prints the estimated model in tabular
form -- by default as LaTeX, but as RTF if the --rtf flag is given. If a
filename is specified using the -f flag output goes to that file, otherwise
it goes to a file with a name of the form model_N.tex (or model_N.rtf),
where N is the number of models estimated to date in the current session.

The further options discussed below are available only when printing the
model as LaTeX.

If the --complete flag is given the LaTeX file is a complete document, ready
for processing; otherwise it must be included in a document.

If you wish alter the appearance of the tabular output, you can specify a
custom row format using the --format flag. The format string must be
enclosed in double quotes and must be tied to the flag with an equals sign.
The pattern for the format string is as follows. There are four fields,
representing the coefficient, standard error, t-ratio and p-value
respectively. These fields should be separated by vertical bars; they may
contain a printf-type specification for the formatting of the numeric value
in question, or may be left blank to suppress the printing of that column
(subject to the constraint that you can't leave all the columns blank). Here
are a few examples:

	--format="%.4f|%.4f|%.4f|%.4f"
	--format="%.4f|%.4f|%.3f|"
	--format="%.5f|%.4f||%.4f"
	--format="%.8g|%.8g||%.4f"

The first of these specifications prints the values in all columns using 4
decimal places. The second suppresses the p-value and prints the t-ratio to
3 places. The third omits the t-ratio. The last one again omits the t, and
prints both coefficient and standard error to 8 significant figures.

Once you set a custom format in this way, it is remembered and used for the
duration of the gretl session. To revert to the default format you can use
the special variant --format=default.

Menu path:    Model window, /LaTeX

# textplot Graphs

Argument:   varlist 
Options:    --time-series (plot by observation)
            --one-scale (force a single scale)
            --tall (use 40 rows)

Quick and simple ASCII graphics. Without the --time-series flag, varlist
must contain at least two series, the last of which is taken as the variable
for the x axis, and a scatter plot is produced. In this case the --tall
option may be used to produce a graph in which the y axis is represented by
40 rows of characters (the default is 20 rows).

With the --time-series, a plot by observation is produced. In this case the
option --one-scale may be used to force the use of a single scale; otherwise
if varlist contains more than one series the data may be scaled. Each line
represents an observation, with the data values plotted horizontally.

See also "gnuplot".

# tobit Estimation

Arguments:  depvar indepvars 
Options:    --llimit=lval (specify left bound)
            --rlimit=rval (specify right bound)
            --vcv (print covariance matrix)
            --robust (robust standard errors)
            --cluster=clustvar (see "logit" for explanation)
            --verbose (print details of iterations)

Estimates a Tobit model, which may be appropriate when the dependent
variable is "censored". For example, positive and zero values of purchases
of durable goods on the part of individual households are observed, and no
negative values, yet decisions on such purchases may be thought of as
outcomes of an underlying, unobserved disposition to purchase that may be
negative in some cases.

By default it is assumed that the dependent variable is censored at zero on
the left and is uncensored on the right. However you can use the options
--llimit and --rlimit to specify a different pattern of censoring. Note that
if you specify a right bound only, the assumption is then that the dependent
variable is uncensored on the left.

The Tobit model is a special case of interval regression, which is supported
via the "intreg" command.

Menu path:    /Model/Limited dependent variable/Tobit

# tsls Estimation

Arguments:  depvar indepvars ; instruments 
Options:    --no-tests (don't do diagnostic tests)
            --vcv (print covariance matrix)
            --robust (robust standard errors)
            --cluster=clustvar (clustered standard errors)
            --liml (use Limited Information Maximum Likelihood)
            --gmm (use the Generalized Method of Moments)
Example:    tsls y1 0 y2 y3 x1 x2 ; 0 x1 x2 x3 x4 x5 x6

Computes Instrumental Variables (IV) estimates, by default using two-stage
least squares (TSLS) but see below for further options. The dependent
variable is depvar, indepvars is the list of regressors (which is presumed
to include at least one endogenous variable); and instruments is the list of
instruments (exogenous and/or predetermined variables). If the instruments
list is not at least as long as indepvars, the model is not identified.

In the above example, the ys are endogenous and the xs are the exogenous
variables. Note that exogenous regressors should appear in both lists.

Output for two-stage least squares estimates includes the Hausman test and,
if the model is over-identified, the Sargan over-identification test. In the
Hausman test, the null hypothesis is that OLS estimates are consistent, or
in other words estimation by means of instrumental variables is not really
required. A model of this sort is over-identified if there are more
instruments than are strictly required. The Sargan test is based on an
auxiliary regression of the residuals from the two-stage least squares model
on the full list of instruments. The null hypothesis is that all the
instruments are valid, and suspicion is thrown on this hypothesis if the
auxiliary regression has a significant degree of explanatory power. For a
good explanation of both tests see chapter 8 of Davidson and MacKinnon
(2004).

For both TSLS and LIML estimation, an additional test result is shown
provided that the model is estimated under the assumption of i.i.d. errors
(that is, the --robust option is not selected). This is a test for weakness
of the instruments. Weak instruments can lead to serious problems in IV
regression: biased estimates and/or incorrect size of hypothesis tests based
on the covariance matrix, with rejection rates well in excess of the nominal
significance level (Stock, Wright and Yogo, 2002). The test statistic is the
first-stage F-test if the model contains just one endogenous regressor,
otherwise it is the smallest eigenvalue of the matrix counterpart of the
first stage F. Critical values based on the Monte Carlo analysis of Stock
and Yogo (2003) are shown when available.

The R-squared value printed for models estimated via two-stage least squares
is the square of the correlation between the dependent variable and the
fitted values.

For details on the effects of the --robust and --cluster options, please see
the help for "ols".

As alternatives to TSLS, the model may be estimated via Limited Information
Maximum Likelihood (the --liml option) or via the Generalized Method of
Moments (--gmm option). Note that if the model is just identified these
methods should produce the same results as TSLS, but if it is
over-identified the results will differ in general.

If GMM estimation is selected, the following additional options become
available:

  --two-step: perform two-step GMM rather than the default of one-step.

  --iterate: Iterate GMM to convergence.

  --weights=Wmat: specify a square matrix of weights to be used when
  computing the GMM criterion function. The dimension of this matrix must
  equal the number of instruments. The default is an appropriately sized
  identity matrix.

Menu path:    /Model/Instrumental variables

# var Estimation

Arguments:  order ylist [ ; xlist ] 
Options:    --nc (do not include a constant)
            --trend (include a linear trend)
            --seasonals (include seasonal dummy variables)
            --robust (robust standard errors)
            --robust-hac (HAC standard errors)
            --quiet (skip output of individual equations)
            --silent (don't print anything)
            --impulse-responses (print impulse responses)
            --variance-decomp (print variance decompositions)
            --lagselect (show criteria for lag selection)
Examples:   var 4 x1 x2 x3 ; time mydum
            var 4 x1 x2 x3 --seasonals
            var 12 x1 x2 x3 --lagselect

Sets up and estimates (using OLS) a vector autoregression (VAR). The first
argument specifies the lag order -- or the maximum lag order in case the
--lagselect option is given (see below). The order may be given numerically,
or as the name of a pre-existing scalar variable. Then follows the setup for
the first equation. Do not include lags among the elements of ylist -- they
will be added automatically. The semi-colon separates the stochastic
variables, for which order lags will be included, from any exogenous
variables in xlist. Note that a constant is included automatically unless
you give the --nc flag, a trend can be added with the --trend flag, and
seasonal dummy variables may be added using the --seasonals flag.

While a VAR specification usually includes all lags from 1 to a given
maximum, it is possible to select a specific set of lags. To do this,
substitute for the regular (scalar) order argument either the name of a
predefined vector or a comma-separated list of lags, enclosed in braces. We
show below two ways of specifying that a VAR should include lags 1, 2 and 4
(but not lag 3):

	var {1,2,4} ylist
	matrix p = {1,2,4}
	var p ylist

A separate regression is reported for each variable in ylist. Output for
each equation includes F-tests for zero restrictions on all lags of each of
the variables, an F-test for the significance of the maximum lag, and, if
the --impulse-responses flag is given, forecast variance decompositions and
impulse responses.

Forecast variance decompositions and impulse responses are based on the
Cholesky decomposition of the contemporaneous covariance matrix, and in this
context the order in which the (stochastic) variables are given matters. The
first variable in the list is assumed to be "most exogenous" within-period.
The horizon for variance decompositions and impulse responses can be set
using the "set" command. For retrieval of a specified impulse response
function in matrix form, see the "irf" function.

If the --robust option is given, standard errors are corrected for
heteroskedasticity. Alternatively, the --robust-hac option can be given to
produce standard errors that are robust with respect to both
heteroskedasticity and autocorrelation (HAC). In general the latter
correction should not be needed if the VAR includes sufficient lags.

If the --lagselect option is given, the first parameter to the var command
is taken as the maximum lag order. Output consists of a table showing the
values of the Akaike (AIC), Schwartz (BIC) and Hannan-Quinn (HQC)
information criteria computed from VARs of order 1 to the given maximum.
This is intended to help with the selection of the optimal lag order. The
usual VAR output is not presented. The table of information criteria may be
retrieved as a matrix via the $test accessor.

Menu path:    /Model/Time series/Vector autoregression

# varlist Dataset

Options:    --scalars (list scalars)
            --accessors (list accessor variables)

By default, prints a listing of the (series) variables currently available.
"list" and "ls" are synonyms.

If the --scalars option is given, prints a listing of any currently defined
scalar variables and their values. Otherwise, if the --accessors option is
given, prints a list of the internal variables currently available via
accessors such as "$nobs" and "$uhat".

# vartest Tests

Arguments:  series1 series2 

Calculates the F statistic for the null hypothesis that the population
variances for the variables series1 and series2 are equal, and shows its
p-value.

Menu path:    /Tools/Test statistic calculator

# vecm Estimation

Arguments:  order rank ylist [ ; xlist ] [ ; rxlist ] 
Options:    --nc (no constant)
            --rc (restricted constant)
            --uc (unrestricted constant)
            --crt (constant and restricted trend)
            --ct (constant and unrestricted trend)
            --seasonals (include centered seasonal dummies)
            --quiet (skip output of individual equations)
            --silent (don't print anything)
            --impulse-responses (print impulse responses)
            --variance-decomp (print variance decompositions)
Examples:   vecm 4 1 Y1 Y2 Y3
            vecm 3 2 Y1 Y2 Y3 --rc
            vecm 3 2 Y1 Y2 Y3 ; X1 --rc
            See also denmark.inp, hamilton.inp

A VECM is a form of vector autoregression or VAR (see "var"), applicable
where the variables in the model are individually integrated of order 1
(that is, are random walks, with or without drift), but exhibit
cointegration. This command is closely related to the Johansen test for
cointegration (see "coint2").

The order parameter to this command represents the lag order of the VAR
system. The number of lags in the VECM itself (where the dependent variable
is given as a first difference) is one less than order.

The rank parameter represents the cointegration rank, or in other words the
number of cointegrating vectors. This must be greater than zero and less
than or equal to (generally, less than) the number of endogenous variables
given in ylist.

ylist supplies the list of endogenous variables, in levels. The inclusion of
deterministic terms in the model is controlled by the option flags. The
default if no option is specified is to include an "unrestricted constant",
which allows for the presence of a non-zero intercept in the cointegrating
relations as well as a trend in the levels of the endogenous variables. In
the literature stemming from the work of Johansen (see for example his 1995
book) this is often referred to as "case 3". The first four options given
above, which are mutually exclusive, produce cases 1, 2, 4 and 5
respectively. The meaning of these cases and the criteria for selecting a
case are explained in the Gretl User's Guide.

The optional lists xlist and rxlist allow you to specify sets of exogenous
variables which enter the model either unrestrictedly (xlist) or restricted
to the cointegration space (rxlist). These lists are separated from ylist
and from each other by semicolons.

The --seasonals option, which may be combined with any of the other options,
specifies the inclusion of a set of centered seasonal dummy variables. This
option is available only for quarterly or monthly data.

The first example above specifies a VECM with lag order 4 and a single
cointegrating vector. The endogenous variables are Y1, Y2 and Y3. The second
example uses the same variables but specifies a lag order of 3 and two
cointegrating vectors; it also specifies a "restricted constant", which is
appropriate if the cointegrating vectors may have a non-zero intercept but
the Y variables have no trend.

Following estimation of a VECM some special accessors are available:
$jalpha, $jbeta and $jvbeta retrieve, respectively, the α and beta matrices
and the estimated variance of beta. For retrieval of a specified impulse
response function in matrix form, see the "irf" function.

Menu path:    /Model/Time series/VECM

# vif Tests

Must follow the estimation of a model which includes at least two
independent variables. Calculates and displays the Variance Inflation
Factors (VIFs) for the regressors. The VIF for regressor j is defined as

  1/(1 - Rj^2)

where R_j is the coefficient of multiple correlation between regressor j and
the other regressors. The factor has a minimum value of 1.0 when the
variable in question is orthogonal to the other independent variables.
Neter, Wasserman, and Kutner (1990) suggest inspecting the largest VIF as a
diagnostic for collinearity; a value greater than 10 is sometimes taken as
indicating a problematic degree of collinearity.

Menu path:    Model window, /Tests/Collinearity

# wls Estimation

Arguments:  wtvar depvar indepvars 
Options:    --vcv (print covariance matrix)
            --robust (robust standard errors)
            --quiet (suppress printing of results)

Computes weighted least squares (WLS) estimates using wtvar as the weight,
depvar as the dependent variable, and indepvars as the list of independent
variables. Let w denote the positive square root of wtvar; then WLS is
basically equivalent to an OLS regression of w * depvar on w * indepvars.
The R-squared, however, is calculated in a special manner, namely as

  R^2 = 1 - ESS / WTSS

where ESS is the error sum of squares (sum of squared residuals) from the
weighted regression and WTSS denotes the "weighted total sum of squares",
which equals the sum of squared residuals from a regression of the weighted
dependent variable on the weighted constant alone.

If wtvar is a dummy variable, WLS estimation is equivalent to eliminating
all observations with value zero for wtvar.

Menu path:    /Model/Other linear models/Weighted Least Squares

# xcorrgm Statistics

Arguments:  series1 series2 [ order ] 
Option:     --plot=mode-or-filename (see below)
Example:    xcorrgm x y 12

Prints and graphs the cross-correlogram for series1 and series2, which may
be specified by name or number. The values are the sample correlation
coefficients between the current value of series1 and successive leads and
lags of series2.

If an order value is specified the length of the cross-correlogram is
limited to at most that number of leads and lags, otherwise the length is
determined automatically, as a function of the frequency of the data and the
number of observations.

By default, a plot of the cross-correlogram is produced: a gnuplot graph in
interactive mode or an ASCII graphic in batch mode. This can be adjusted via
the --plot option. The acceptable parameters to this option are none (to
suppress the plot); ascii (to produce a text graphic even when in
interactive mode); display (to produce a gnuplot graph even when in batch
mode); or a file name. The effect of providing a file name is as described
for the --output option of the "gnuplot" command.

Menu path:    /View/Cross-correlogram
Other access: Main window pop-up menu (multiple selection)

# xtab Statistics

Arguments:  ylist [ ; xlist ] 
Options:    --row (display row percentages)
            --column (display column percentages)
            --zeros (display zero entries)
            --matrix=matname (use frequencies from named matrix)

Displays a contingency table or cross-tabulation for each combination of the
variables included in ylist; if a second list xlist is given, each variable
in ylist is cross-tabulated by row against each variable in xlist (by
column). Variables in these lists can be referenced by name or by number.
Note that all the variables must have been marked as discrete.
Alternatively, if the --matrix option is given, treat the named matrix as a
precomputed set of frequencies and display this as a cross-tabulation.

By default the cell entries are given as frequency counts. The --row and
--column options (which are mutually exclusive), replace the counts with the
percentages for each row or column, respectively. By default, cells with a
zero count are left blank; the --zeros option, which has the effect of
showing zero counts explicitly, may be useful for importing the table into
another program, such as a spreadsheet.

Pearson's chi-square test for independence is displayed if the expected
frequency under independence is at least 1.0e-7 for all cells. A common rule
of thumb for the validity of this statistic is that at least 80 percent of
cells should have expected frequencies of 5 or greater; if this criterion is
not met a warning is printed.

If the contingency table is 2 by 2, Fisher's Exact Test for independence is
computed. Note that this test is based on the assumption that the row and
column totals are fixed, which may or may not be appropriate depending on
how the data were generated. The left p-value should be used when the
alternative to independence is negative association (values tend to cluster
in the lower left and upper right cells); the right p-value should be used
if the alternative is positive association. The two-tailed p-value for this
test is calculated by method (b) in section 2.1 of Agresti (1992): it is the
sum of the probabilities of all possible tables having the given row and
column totals and having a probability less than or equal to that of the
observed table.