Code: Select all
set onestep = setar(t)
group lsetarmodel setar
forecast(model=lsetarmodel,results=fcasts) * 1 ibegin+1+jNot even for one-step ahead? I have read on the Web, static or one-step ahead forecasting is straightforward and uses the estimated equations.TomDoan wrote:There is no closed-form formula for out-of-sample forecasting in a threshold model.
Code: Select all
*===============================
*
* RECURSIVE STATIC ONE-STEP AHEAD OOS FORECASTS
*
*
seed 550103
*
clear(length=2010:10) urfore_1
*
do regend = 2010:10-13, 2010:10
*
@threshtest(thresh=dur,d=1,nreps=100) dur * regend
# constant dur{1 to 4}
*
compute break1=%%breakvalue
disp break1
*
* Estimate the two branches and define equations
*
linreg(smpl=dur{1}<=break1,frml=branch1) dur * regend
# constant dur{1 to 4}
compute rss1=%rss,ndf1=%ndf
linreg(smpl=dur{1}>break1,frml=branch2) dur * regend
# constant dur{1 to 4}
compute rss2=%rss,ndf2=%ndf
compute seesq=(rss1+rss2)/(ndf1+ndf2)
*
*
* Define the forecasting model
*
frml(variance=seesq) tarfrml dur = $
%if(dur{1}<=break1,branch1,branch2)
frml(identity) urid unrate = unrate{1}+dur
group tarmodel tarfrml urid
*
*
* Compute average across simulations
*
compute fstart=regend,fend=regend
set urfore_1 regend regend = 0.0
compute ndraws=5000
do draw=1,ndraws
simulate(model=tarmodel,from=regend,to=regend,results=sims_1,noprint)
set urfore_1 regend regend = urfore_1+sims_1(2)
end do draw
set urfore_1 regend regend = urfore_1/ndraws
*
*
end do regend
*
graph(footer="RECURSIVE STATIC ONE-STEP AHEAD OOS FORECASTS") 2
# unrate regend-13 regend
# urfore_1
*
prin fstart-13 regend unrate urfore_1
Theoretically, what you are doing is generating a sample of size N from the population of values for the process conditional on the last observed value. The "forecast" is the mean of that population. The standard error of forecast is the standard error of that distribution.ac_1 wrote:I've managed to generate STATIC ONE-STEP AHEAD OOS FORECASTS, adjusting via regend+1 in SIMULATE; and have generated DYNAMIC MULTISTEP AHEAD OOS FORECASTS saving only the nth forecast step.
The question is how to simulate the forecast SE's, which are method specific e.g. SETAR-AR(p), for prediction intervals?
Thanks.TomDoan wrote: Theoretically, what you are doing is generating a sample of size N from the population of values for the process conditional on the last observed value. The "forecast" is the mean of that population. The standard error of forecast is the standard error of that distribution.
Code: Select all
compute ndraws=5000
do draw=1,ndraws
simulate(model=tarmodel,from=regend+1,to=regend+1,results=sims_1,noprint)
set urfore_1 regend+1 regend+1 = urfore_1+sims_1(2)
set urfore_1_M regend+1 regend+1 = urfore_1/draw
set urfore_1_VAR regend+1 regend+1 = urfore_1_VAR + ((sims_1(2)-urfore_1_M)^2)/draw
set urfore_1_SE regend+1 regend+1 = sqrt(urfore_1_VAR)
end do draw
A better attemptTomDoan wrote:No. The averages are done outside the loop. Inside, you accumulate the sums and sums of squares (since you don't know the mean, you just to raw sums of squares), then convert to mean and variance/standard error outside the loop.
. Correct?
Code: Select all
compute ndraws=5000
do draw=1,ndraws
simulate(model=tarmodel,from=regend+1,to=regend+1,results=sims_1,noprint)
set urfore_1 regend+1 regend+1 = urfore_1+sims_1(2)
set urfore_1_SS regend+1 regend+1 = urfore_1_SS+(sims_1(2))^2
end do draw
set urfore_1_M regend+1 regend+1 = urfore_1/ndraws
set urfore_1_MSS regend+1 regend+1 = urfore_1_SS/ndraws
set urfore_1_SE regend+1 regend+1 = sqrt(urfore_1_MSS-(urfore_1_M)^2)