From: <stoffer_at_pitt.edu>

Date: Mon 03 Apr 2006 - 03:59:18 GMT

Date: Mon 03 Apr 2006 - 03:59:18 GMT

I have written before, but to no avail. I have found two minor
problems with fitting time series models with R. The thing is, they
may be solved with MINOR adjustments to the code.

I have posted these problems with detailed examples here: http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm

Briefly, the problems are

(1) When fitting time series models when there is an AR term present,

the output says it's giving you the estimate of the intercept, when,
in fact, it's giving you the estimate of the mean. These are NOT the
same when an AR term is present. This occurs in everything I've seen,
from ar.ols(), ar.mle(), ... and in arima().

(2) When fitting ARIMA models when there differencing, the constant

term (intercept) is assumed to be zero. This ignores the possibility
that there is drift. In this case, the estimation is WRONG.

Details and examples are at the url mentioned above. To remedy (1), simply change "intercept" to "mean" or actually list the intercept instead of the mean. To remedy (2), allow for the option to include an intercept. I tried using xreg in the arima command, but could not come up with a proper solution to this problem.

Thank you for your time.

D. Stoffer

-- -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- David S. Stoffer Department of Statistics University of Pittsburgh Pittsburgh, PA 15260 phone: [412] 624-8496 fax: [412] 648-8814 email: stoffer@pitt.edu web: http://www.stat.pitt.edu/stoffer voice: hey dave ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-develReceived on Mon Apr 03 14:24:36 2006

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