That response helps. There are two variance decompositions, one based on moments_varendo command and the other is based on the stochastic simulation command. What you are saying is that two differ because the former is based on posterior mean and the latter is based on posterior mode.
If they differ too much you say that convergence is an issue but my multivariate m2 and m3 plots do not show any such symptoms. Blue and red curves converge and become flat. The programme runs smoothly with 5 Markov chains and 20k mh replics without any complaints.
The issue here is that if I change the observable for estimation, the former VD (based on moments_varendo is very sensitive but the latter VD (based on stoch simul ) is not. I think it may be because that mean is sensitive to outliers but mode is not. It is just a wide guess.
However, my mean VDs make more economic sense. The mode VDs seem to be dominated by one shock.