I presume that your problem is in quantitative macroeconomics and that your sample size is limited. Are your variables stationary. If not you may need to use a VECM or if there is no cointegration work in first differences.
My choice of variables would in the first instance be determined by economic theory and the number of lags by an information criterion or by testing down from a general model. You might use Granger Causality tests to reduce the model if necessary. Otherwise a lot depends on the properties of your model. For forecasting purposes a BVAR might be a better solution. Best Regards John Frain 2008/8/12 Zhang Yanwei - Princeton-MRAm <[EMAIL PROTECTED]>: > Hi all, > I got another VAR question here and really appreciate if somebody would help > me out :) > I have five time series, say A,B,C,D,E. My objective is to predict the series > A using the rest, that is, B, C, D and E. A Vector Autoregression Model > should work here. But first of all, I should select which series of B, C, D > and E to be include in the VAR model, as well as the number of lags. I wonder > If someone would give some advice on such a series selection procedure. > Thanks so much. > > Sincerely, > Yanwei Zhang > Department of Actuarial Research and Modeling > Munich Re America > Tel: 609-275-2176 > Email: [EMAIL PROTECTED]<mailto:[EMAIL PROTECTED]> > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- John C Frain Trinity College Dublin Dublin 2 Ireland www.tcd.ie/Economics/staff/frainj/home.html mailto:[EMAIL PROTECTED] mailto:[EMAIL PROTECTED] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
