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# open the dataset

open nile.gdt

# set up the local linear trend model with the Nile data

Nile = ksetup({nile}, {1}, {1}, {1})
Nile.diffuse = 1
Nile.obsvar = {1}

# initialize the parameters via the reduced form
# \Delta y_t = \eta_t + \Delta \epsilon_t

series dnile = diff(nile)
scalar    vd = var(dnile)
scalar  veps = -cov(dnile,dnile(-1))
scalar s_eps = sqrt(veps)
scalar s_eta = sqrt(vd - 2*veps)

# estimate the variances via MLE

scalar err = 0
mle loglik = err ? NA : Nile.llt
    matrix Nile.statevar = s_eta^2
    matrix Nile.obsvar = s_eps^2
    err = kfilter(&Nile)
    params s_eta s_eps
end mle

# extract the smoothed trend

ksmooth(&Nile)
series nile_trend = Nile.state

# plot series with trend and associated 95% confidence band

series nile_trend_se = sqrt(Nile.statevar)
series hi = nile_trend + 1.96 * nile_trend_se
series lo = nile_trend - 1.96 * nile_trend_se
list X = nile nile_trend hi lo

plot X
    options with-lines time-series
    literal set linetype 3 lc rgb "#00c0c0"
    literal set linetype 4 lc rgb "#00c0c0"
    literal set key right top
    printf "set title \"%s\"", "Nile data and trend with 95\% confidence band"
end plot --output=display