Your entering into a complex danger zone here because you really need to check first if all the dependent and independent variables are stationary. Otherwise, your lm results are meaningless ( you're estimation a spurious regression ). I would look at Bernhard Pfaff's yellow book or any other decent time series econometrics text ( hayashi, hamilton ) for more on this topic. It's a quite complex problem you are working on so you need to get familiar with the cointegration/unit root concepts, if you aren't already.
-----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of bereket weldeslassie Sent: Thursday, March 20, 2008 11:54 AM To: [EMAIL PROTECTED]; r-help@r-project.org Subject: [R] Fwd: time series regression Hi Everyone, One more information to my question. I am trying to do a time series regression using the lm function. *My intention is to investigate the relationship between a dependent time series variable and several independent time series variables.* According to the durbin watson test the errors are autocorrelated. And then I tried to use the gls function to accomodate for the autocorrelated errors. My question is how do I know what ARMA process (order) to use in the gls function? Or is there any other way to do the time series regression in R? I highly appreciate your help. Thanks, Bereket [[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. ______________________________________________ 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.
