Bayesian Estimation in Dynare versus B-VAR

[Daniel Bendel]

Dear Users,

I know that a DSGE model is in the end a VAR model. Thus it should be possible to estimate the parameters as one would estimate them in a VAR model. When doing a bayesian VAR estimation, one can estimate the parameters by first running a OLS regression on the model equations separately. Then using the results as priors for the bayesian estimation. Then runing the MCMC to get the posteriors.

My question is now the following. Why does Dynare first maximzes the liklihood function before runing the MCMC, and does not simply follow the approach in estimating B-VAR models?

Best,
Daniel

[jpfeifer]

I am not sure I understand the question.

First of all, the DSGE model generally is not a VAR. It is a VARMA. If you are lucky, you can represent it as a finite order VAR in your observables (see e.g. the ABCD-test of Fernandez-Villaverde et al. and the fundamentalness literature around Lippi-Reichlin).
Second, the coefficients in your VAR are unrestricted. This differs from the DSGE model that will feature cross-equation restrictions (e.g. the discount factor entering for example coefficients (1,1) and (2,3) must be the same). If you want to account for MA components and cross-equation restrictions, you cannot use OLS and/or equation by equation estimation.