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Priors about Observables in Vector Autoregressions

Marek Jarociński and Albert Marcet ()

No 684, Working Papers from Barcelona Graduate School of Economics

Abstract: Standard practice in Bayesian VARs is to formulate priors on the autore- gressive parameters, but economists and policy makers actually have priors about the behavior of observable variables. Our proposal is to use prior infor- mation on observables systematically. We show how this kind of prior can be used under strict probability theory principles. We state the inverse problem to be solved and we propose a numerical algorithm that works well in practical situations with a large number of parameters. We prove various convergence theorems for the algorithm. Using examples from the VAR literature, we show how priors on observables can address a priori weaknesses of standard priors, serving as a cross check and an alternative formulation.

Keywords: vector autoregression; Bayesian estimation; prior about observables; inverse problem; monetary policy shocks (search for similar items in EconPapers)
JEL-codes: C11 C22 C32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets
Date: 2013-03
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Related works:
Journal Article: Priors about observables in vector autoregressions (2019) Downloads
Working Paper: Priors about Observables in Vector Autoregressions (2013) Downloads
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