[R] Regression with time-dependent coefficients

[achronal at securenym.net]

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