On Fri, 8 Jul 2005, yyan liu wrote: > Hi: > I have two time series y(t) and x(t). I want to > regress Y on X. Because Y is a time series and may > have autocorrelation such as AR(p), so it is not > efficient to use OLS directly. The model I am trying > to fit is like > Y(t)=beta0+beta1*X(t)+rho*Y(t-1)+e(t) > > e(t) is iid normal random error. Anybody know whether > there is a function in R can fit such models? The > function can also let me specify how many beta's and > rho's I can have in the model.
If you want to estimate the model by ML, you can use arima() and specify further regressors via the `xreg' argument. Estimation by OLS can be done via lm(), but that typically requires setting up the lags yourself. More convenient interfaces are provided in the `dyn' package by Gabor Grothendieck and my `dynlm' package. Z > Thx a lot! > > liu > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
