Sven,
Should you not should consider using tssmooth exponential, arima, or
prais rather than ols reg, unless you have a theoretical reason for
showing the defects of
not controlling for autocorrelation in the series?
Regards,
Robert
Robert A. Yaffee, Ph.D.
Research Professor
Shirley M. Ehrenkranz
School of Social Work
New York University
home address:
Apt 19-W
2100 Linwood Ave.
Fort Lee, NJ
07024-3171
Phone: 201-242-3824
Fax: 201-242-3825
[email protected]
----- Original Message -----
From: "Svein.Oskar Lauvsnes" <[email protected]>
Date: Friday, December 8, 2006 3:03 am
Subject: st: Time series: VECM with recursive window forecasts
> Hi,
> I am investigating the predictive abilities of macrovariables on
> stock market returns. So far I have made 1-step ahead predictions
> from single equation models, keeping the starting point fixed and
> for each new regression extending the dataset by one observation. I
> would like to compare the single equation forecasts with forecasts
> from a system of equations such as a vector error correction model
> and a VAR. I have used the following program for my forecasts:
>
> gen time = _n
> tsset time
>
>
>
> capture program drop rforecast
> program rforecast, rclass
> syntax [if]
> regress dose l.dose dnib `if'
> summ time if e(sample)
> local last = r(max)
> local fcast = _b[_cons] + _b[L.dose]*dose[`last']///
> + _b[dnib]*nib[`last'+1]
>
> return scalar forecast = `fcast'
> return scalar actual = dose[`last' +1]
> end
>
> rolling actual=r(actual) forecast=r(forecast), recursive ///
> window(149) saving(myrolling, replace): rforecast
>
> use myrolling, clear
> list in 1/100
>
> Hopefully, the program will work on a VECM by substituting the
> sentences in bold. How should I modify my program to do rolling
> window estimation/forecasting using a VECM? I suppose the number of
> cointegrating vectors and lags would have to be fixed.
>
> Sincerely
>
> Svein Lauvsnes
> Bodoe Graduate School of Business, Norway
>
>
>
>
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