The Cochrane Orcutt is probably an outdated approach to deal with autocorrelation and it is rather easy to write code.
Why don't you use a direct likelihood-based approach? For gaussian data see the arima() function in ts package, or the Jim Lindsey's packages (for instance the gar() function in the repeated package at http://alpha.luc.ac.be/~lucp0753/rcode.html Also for GLM you can have a look at the Thomas Lumley's weave package that implements different standard error estimators http://faculty.washington.edu/tlumley/weave.html hope this helps you, best, vito ----- Original Message ----- From: Wayne Jones <[EMAIL PROTECTED]> To: <[EMAIL PROTECTED]> Sent: Wednesday, November 19, 2003 11:36 AM Subject: [R] Correction for first order autocorrelation in OLS residuals > Hi there fellow R-users, > > Can anyone tell me if there exits an R package that deals with serial > correlation in the residuals of an lm model. > Perhaps, using the Cochrane Orcutt or Praise Wilson methods? > > Thanks, > > Wayne > > > Dr Wayne R. Jones > Senior Statistician / Research Analyst > KSS Limited > St James's Buildings > 79 Oxford Street > Manchester M1 6SS > Tel: +44(0)161 609 4084 > Mob: +44(0)7810 523 713 > > > > > KSS Ltd > Seventh Floor St James's Buildings 79 Oxford Street Manchester M1 6SS England > Company Registration Number 2800886 > Tel: +44 (0) 161 228 0040 Fax: +44 (0) 161 236 6305 > mailto:[EMAIL PROTECTED] http://www.kssg.com > > > The information in this Internet email is confidential and m...{{dropped}} > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help