I’d like to carry out models selection by comparing my MS-BSVAR models Bayesian Information Criterion. For this I need the posterior log-likelihood of the model. Any ways to get this from the ms_estimation command?

Thanks for your previous answer. I’m still wondering how to use the restrictions on the lagged coefficients in the SVAR a la Sims, Wagonner, and Zha (2008). According to the user guide, it can be called by svar_restriction(SWZ) but that doesn’t seem to work. Any hint how I can practically use it?

which means that in equation 1, the lagged output coefficient (y_{t-1}) is equal to the lagged inflation coefficient (pi_{t-1}), and in equation 2, the output coefficient at lag 2 is equal to 0.

in_P1 is the number of proposal draws such that the density of the proposal is greater than
or equal to the density (properly scaled!) of the posterior. in_P2 is the number of
posterior draws such that the density (properly scaled!) of the posterior is greater than
or equal to the density of the proposal. Both numbers should be somewhat larger than zero.
Ideally both should also be somewhat less than the maximum possible value, though in
practice this rarely happens.

“ms_compute_mdd” does not report the likelihood but an approximation of the marginal likelihood, also called marginal data density (mdd). It computes it using three different methods: Bridge, Mueller, and Sims, Waggoner and Zha (2008,JoE). In your case, the two former methods are fine. The Bridge method is the standard one.