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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