Hi, I was wondering if someone might be willing to indulge a question about R and the estimation of a linear regression with time-varying coefficients.
The model I am trying to estimate is of the form: y(t) = beta(t) * x(t) + v(t) beta(t) = gamma * beta(t-1) + w(t) where gamma is a constant, v(t) and w(t) are Gaussian innovations, and where y(t) and x(t) are univariate time series that are known. I would like to estimate gamma and the variance of v(t) and w(t). I have tried using the following packages with no success: sspir, dse (specifically dse1) and dlm. In each case, any attempt to specify the above model meets with some sort of error. Hence, I have not been able to estimate the model. I am using "manufactured" data, i.e., I assume a value for gamma, beta(0), and values for the variances of v(t) and w(t), so I know what result I should attain. I would really appreciate any help on how to appropriately specify the above model in any of the packages I mentioned. Best regards, -Paul ______________________________________________ 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.
