David Stoffer describes some challenges with R's output when fitting
ARIMA models for different orders (see Issue 2 at
http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm). R doesn't fit an
intercept in the model if there is any differencing. David describes a
workaround using the xreg parameter to force R to calculate an
intercept.
Assume I have a variable y and 3 explanatory variables a, b and c.
No intercept would be produced for the model .... fit = arima(y,
order=c(1,1,0), xreg=c(a,b,c))
1. If I wish to force an intercept to be output is the following
correct?
intercept = 1:length(y)
fit2 = arima(y, order=c(1,1,0), xreg=c(intercept, a, b, c))
2. If 1 is correct, is the following code equivalent?
data = ts.intersect(diff(y),intercept, a,b,c)
fit3 = arima(data[,1], order=c(1,0,0), xreg=[,c(2:5)])
3. If I fit 2 and find the intercept is not significant would it be
correct to use the following?
fit = arima(y, order=c(1,1,0), xreg=c(a,b,c))
Thanks for your help
Kind regards
Pete
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