In the nlme package you can find the gls() function to account for autocorrelation over time using corAR1. Syntax might look something like this:
fm1 <- gls(response ~ IV, long, correlation=corAR1(form=~1|ID), method='ML') You can also use weights() for heteroscedasticity. -Harold -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of David Hugh-Jones Sent: Thursday, February 10, 2005 12:15 PM To: r-help@stat.math.ethz.ch Subject: [R] correcting for autocorrelation in models with panel data? Hi I have some panel data for the 50 US states over about 25 years, and I would like to test a simple model via OLS, using this data. I know how to run OLS in R, and I think I can see how to create Panel Corrected Standard Errors using http://jackman.stanford.edu/classes/350C/pcse.r What I can't figure out is how to correct for autocorrelation over time. I have found a lot of R stuff on time series models but they all seem focused on predicting a single variable from its previous values. Can anyone explain to me how to detect and get round autocorrelation? Is there a package for panel data that I have missed? I appreciate that this is probably just as much about my ignorance of econometrics as about R itself! Cheers David ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html