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(a)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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