Hi All,
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 I have also calculated the coefficients in some way.
For predicting say, the 22nd value for Loss (i.e.Loss_22), how do I input
the value of the W_21 variable, because this variable (W_21) is generally
proxied via the error in prediction (i.e. observed - predicted value of
Loss) in the 21st stage, but since I don't know the observed value of Y_21,
there is no way to calculate the error in this stage (21st) .
Also, the way I have calculated the coefficients in the above model is
non-standard (differencing, bootstrapping, ridge regression), hence I
cannot use the general ARMA codes in R for prediction (or can I? (Please
let me know if this is possible))
So it would be great if you could help on this method or let me know what
algorithm R uses to solve this problem because such models are indeed used
in practice.
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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