I have a different but similar problem where 2 series appear to be recursively linked  Yt=f(Xt ...) ; Xt=g(Yt-1....) .  How to generate dynamic roll-forward forecasts from the two separate equations when both Tt and Xt are of interest.


On Thu, 7 Jan 2021 at 20:30, Sven Schreiber <svetosch@gmx.net> wrote:
Am 07.01.2021 um 20:23 schrieb Burak Korkusuz:
> Hi,
> Below is an example for one-step ahead forecasting that I use the first
> 5 obs. for initial sample and then forecast one-step-ahead by rolling ahead.
> My question is that how should I change my code to get 5-step-ahead
> rolling forecast.
> Thanks,
>
> open denmark.gdt
> set verbose off
> series frcst = NA
> loop i=1..20 -q         #out-of-sample (20 observations)#
>      smpl 1+i 5+i        #initial sample (5 observations)#
>      ols LRM const LRY
>      fcast 6+i 6+i    #one-step-ahead-rolling-windows-forecasting#
>      frcst[6 + i] = $fcast
> endloop
>

Hi, two questions first: Do you really want to have a rolling sample
with a constant sample length of 5 obs? Or rather move only the end obs?
Secondly, do you really want to have no lagged variables in your
equation? While technically feasible, a "forecast" from an equation
where all regressors except the constant are unknown for the next period
doesn't give anything really meaningful.

cheers
sven
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