Robert,
thanks for a rapid answer. I agree that in general arima models (perhaps applying genreal to specific modeling) would be more suitable than ols for time series in order to get a "congruent model", i.e. no serial correlation/heterosced, normality in residuals. However, in this case I first intend to compare a simple AR(1) model (close to a pure random walk) with an extended model (also single equation) including some macrovariables regarding predictive abilities. For this purpose I might as well use ols, regressing the change in the log of the dependent variable on its 1st lag instead of formulating an AR(1), which would be the same. Also, I also intend to compare my results with those of Rapach et al (2005)
The second step in this exercise is to estimate a vecm system, and again compare predictive abilities (see e.g. McRae et al, 2002). It is in this step that I need some help to automatize the estimation and forecasting process. Here too, congruency is not considered, I intend to estimate a vecm with a fixed number of lags and cointegrating vectors for each estimation. Of course, I will check subsamples to see if they differ greatly regarding these assumptions. When estimating the vecm on the entire sample, an eigenvalue test show that they are fairly stable throughout. Also, there are arguments for using only the cointegrating vector with the largest eigenvalue (See Johansen/Juselius).
So, comparing predictive ability by increasing the informational content in a parsimonious model is the main topic. What do you think about this? Any programming suggestions would be great.
Regards,
Svein.
>>> [email protected] 08.12.2006 14:57 >>>
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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