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
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David S. Stoffer
Department of Statistics
University of Pittsburgh
Pittsburgh, PA 15260
phone: [412] 624-8496
fax: [412] 648-8814
email: stoffer at pitt.edu
web: http://www.stat.pitt.edu/stoffer
voice: hey dave
