Priors about Observables in Vector Autoregressions
Marek Jarociński and
Albert Marcet ()
No 684, Working Papers from Barcelona Graduate School of Economics
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)
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Journal Article: Priors about observables in vector autoregressions (2019)
Working Paper: Priors about Observables in Vector Autoregressions (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:bge:wpaper:684
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