 
Hello, fellow R users,
 
I recently need to estimate a dynamic linear model in the following form:
 
For the measurement equation: 
 
Y_t = F_t * a_t + v_t
 
where Y_t is the observation. It is a 1 by q row vector for each t.
F_t is my forecasting variable. It is a 1 by p row vector.
a_t is my state variable. It is a p by q MATRIX of parameters with each column of the matrix being regression coefficient of a random variable in Y_t. And v is a multivariate normal noise.
 
The state equation is:
 
a_t = a_(t-1) + Omega_t
So a_t is a matrix random walk process. The distribution of Omega_t is a matrix-normal distribution.
 
This model is very well discussed on page 579 of West and Harrison's book on DLM. The key feature being that the state variable is a matrix. I have spent quiet some time with package dlm and even bought the book written by the authors from Amazon. But all the models disussed in the R book for dlm package has the state variable being vectors. (For various reasons, I do not want to stack up the columns of the matrix into a vector.) So I am wondering any body can offer me some hints on how I could proceed? If anybody can show me an example in R that would be fantastic.
 
Happy thanksgiving.
 
Wei


      
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