I am working with a small state space model of the Australian economy. The model consists of three measurement equations:
The starred variable are latent states that I would like to estimate with the Kalman filter.
The authors of the original paper use the following priors
I’ve cast the model into state space form, and have been able to estimate it via maximum likelihood. However, it is a bit fiddly as the state variable y* is sum of two I(1) variables and suffers from the pile up problem (as does the variable z which enters in the r* equation).
I’d like to compare these results with a Bayesian estimation. Can Stan implement this kind of model? If so, could someone please point me to an example where something similar has been done (namely a model with multiple measurement and state equations).