(Cross posted to sci.stat.math & sci.stat.consult)
Usually, the constant variable is included in the covariance matrix.
When you can estimate the parameter then you should be able to have it
in the COV matrix.
And then that should be very easy,
Wolfgang
Thanks for your response. If the constant has to be included, I
generally transform the given data set by subtracting the mean out,
i.e., w_t = z_t - \mu, and then find out the ARMA parameters. Then I
calculate the constant using C = 1- \mu*(1 - Sum_{AR params})
I think the Box-Jenkins section talks about this same procedure, and in
this case, the constant is not included in the var-covar matrix. Isn't
this approach correct?
How would I go about including the constant in the var-covar matrix?
Have you tried a modernish book such as:
Harvey AC (1994) "Forecasting, structural time series models and the Kalman filter" , Cambridge University Press ISBN 0-521-40573-4
...this provides a computational procedure that does not subtract out the mean initially.
David Jones