Inference in Structural Vector Autoregressions When the Identifying Assumptions are Not Fully Believed: Re-evaluating the Role

Christiane Baumeister and James Hamilton

No 12911, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: Reporting point estimates and error bands for structural vector autoregressions that are only set identified is a very common practice. However, unless the researcher is persuaded on the basis of prior information that some parameter values are more plausible than others, this common practice has no formal justification. When the role and reliability of prior information is defended, Bayesian posterior probabilities can be used to form an inference that incorporates doubts about the identifying assumptions. We illustrate how prior information can be used about both structural coefficients and the impacts of shocks, and propose a new distribution, which we call the asymmetric t distribution, for incorporating prior beliefs about the signs of equilibrium impacts in a nondogmatic way. We apply these methods to a three-variable macroeconomic model and conclude that monetary policy shocks were not the major driver of output, inflation, or interest rates during the Great Moderation.

Keywords: Structural vector autoregressions; Set identification; Informative priors; Model uncertainty; Monetary policy; Impulse-response functions; Historical decompositions (search for similar items in EconPapers)
JEL-codes: C11 C32 E52 (search for similar items in EconPapers)
Date: 2018-05
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mac and nep-mon
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Citations: View citations in EconPapers (88)

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