I have the following time series model for prediction purposes



*Loss_t = b1* Loss_(t-1)   +  b2*GDP_t  +  b3*W_(t-1)*  where W_t is the
usual white noise variable.

So this is similar to ARMA(1,1) except that it also contains an extra
predictor, GDP at time t.

I have only 20 observations on each variable except GDP for which I know
till 100 values.

And most importantly,I have also calculated the coefficients in some way
(these I want to use for prediction).



How can I use R to predict the value of Loss_22 (say) because I cannot
manually input the values of the white noise error at time 21 (since I
don't know what the actual observationL_21 is going to be).


I guess the arima() function can be used for this purpose and was going
through it where I found : "When regressors are specified, they are
orthogonalized prior to fitting unless any of the coefficients is fixed ".
I wanted to know exactly how to fix the coefficients to run the prediction
model and get the values of Loss_22,23,.. and so on.


The link to the help-page is as below:

http://stat.ethz.ch/R-manual/R-devel/library/stats/html/arima.html


Appreciate your help.


Thanks,

Preetam




-- 
Preetam Pal
(+91)-9432212774
M-Stat 2nd Year,                                             Room No. N-114
Statistics Division,                                           C.V.Raman
Hall
Indian Statistical Institute,                                 B.H.O.S.
Kolkata.

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