When I was doing Bayesian estimation, only mode_compute=6 works. And the result doesn’t converge. So I use the mode_check. However, I can not understand the meaning of the plots. I wonder how the analyse these plots. Thanks for your help.
The flat likelihood kernel indicates that some parameters are not identified. The parameter eta runs into a bound where the model cannot be solved for some reason. Use options_.debug=1 to get the error code.
Thanks for help. I change two parameters‘ prior distribution， and the mode-check plots are better. But from the plots, it seems that parameter h still can not be identified. Then I used options_.debug=1; identification; at the same time. I got these:
==== Identification analysis ====
Testing prior mean
Evaluating simulated moment uncertainty … please wait
Doing 120 replicas of length 300 periods.
Simulated moment uncertainty … done!
All parameters are identified in the model (rank of H).
All parameters are identified by J moments (rank of J)
==== Identification analysis completed ====
However, there isn’t any result for this command: options_.debug=1
what should I do now? The information means there isn’t identification problem, but the estimation result didn’t converge.