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. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
