Thanks for your reply,
maybe, that one is clearer. This is for one-step ahead forecasting. I am trying to generate 5 (and 22) -step-ahead out-of-sample forecasts using the equation (ols djclose const djclose(-1)) starting of the sample period 1+i  – 100+i. However, the equation is estimated recursively every time and forecasted every time using the new equation fitted values. 


open djclose.gdt
set verbose off
series frcst = NA
loop i=1..200 -q         #out-of-sample (10 observations)#
    smpl 1+i 100+i        #initial sample (5 observations)#
    ols djclose const djclose(-1)      
    fcast 101+i 101+i    #one-step-ahead-rolling-windows-forecasting#
    frcst[101 + i] = $fcast
endloop


On Thursday, January 7, 2021, 09:35:14 PM GMT, Sven Schreiber <svetosch@gmx.net> wrote:


Am 07.01.2021 um 22:16 schrieb Burak Korkusuz:
> I just randomly wrote my code as an example for that dataset to be
> clear. Yes, I want to have a rolling initial sample with a constant.

Hm, still don't really understand what a rolling initial sample is; I'm
asking because gretl's builtin --recursive option might be relevant. But
anyway, just define your horizon, e.g.:

horizon = 5

and replace your fcast lines with the adapted ones:

      fcast 5+i+horizon 5+i+horizon
      frcst[5+i+horizon] = $fcast

Notice, however, that in a model of your type (ols LRM const LRY), the
RHS variable LRY is treated as exogenous. So your "forecast" in this
case does nothing else than take the observed value of LRY five steps
ahead and plugs it into the estimated equation. But that comes from your
model specification, not from the fcast command.

hth,

sven
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