I have one problem on bayesian estimation. I appreciate someone could give me some hint.

The log linearized equations usually contains steady state variables of endogenous variables.

Those steady state values of course depend on deep parameters. During the iteration of estimation, the estimated parameter will be updated and the steady state values should also be updated accordingly. In some models, all steady state values could be express as some function of deep parameters explicitly. So I could replace those steady state by relationship with parameters and estimating those parameters. I am estimating standard matching models. It seems to me it is not possible to do that. I try to solve the steady state of matching model by hand, and I could reduce the system of steady state relationship to one equation and one unkown. But unfortunately it is highly nonlinear, and I could not explicitly express this variable as function of estimated parameters.

I am told that I could take some steady state value as unknown parameter, and estimate it, meanwhile adding some measurement equation. This strategy will get dynare work. I am not sure that is perfect strategy, because some information of steady state relationship is lost.

My question is that, if it is not possible to express steady state values as explicit function of parameters, how could I do bayesian estimation, in particular

make dynare keeping the dependence of steady state values on estimated parameters during iteration. Thanks for your help!!

Justin