My question is about Bayesian VARs (to be specific, how to utilize them in Eviews).
To begin with, I would like to know whether Bayesian VARs are superior to conventional VARs, or when to use Bayesian VARs, rather than conventional VARs.
And suppose that I have to use Bayesian VARs in my research. In Eviews, unlike unrestricted VARs, I have to specify priors, and so on, when employing Bayesian VARs. Are there any "cookbook" (e.g. t-value greater than 2.0) procedure in using Bayesian VARs in Eviews? I will be delighted if you provide me with non-technical lecture notes or spell out the general procedure.
Many thanks in advance,
Mizuki Tsuboi
Classical VARs are more straightforward to apply. Bayesian VARs are not so straightforward to apply. For them one must assume a prior distribution which can be quite subjective. With data this can be updated to obtain the posterior distribution with which to obtain Bayes solution. However if there is a vast amount of historical data the Bayesian approach might be beneficial. The relative performance of the classical frequentist approach and the Bayesian approach depends on the scenario.
I use Eviews 7. There is VAR modelling program but there is no automatic facility for Bayesian VAR modelling. I believe that this is no hindrance because the researcher can improvise and customize the program to suit his/her specific needs if he/she is sufficiently knowledgeable.
BVAR methods are popular approach for achieving shrinkage which helps to solve over-parametrization problems arising from VAR methods. BVAR methods are very straightfirward with EViews 9.
I use Eviews 9 and this version has the command "Bayesian VARs." I appreciate your responses, but I am looking forward the answer, not the response, to my question.
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