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Inference in Structural Vector Autoregressions When the Identifying Assumptions are Not Fully Believed: Re-evaluating the Role of Monetary Policy in Economic Fluctuations

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  • Baumeister, Christiane
  • Hamilton, James

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.

Suggested Citation

  • Baumeister, Christiane & Hamilton, James, 2018. "Inference in Structural Vector Autoregressions When the Identifying Assumptions are Not Fully Believed: Re-evaluating the Role of Monetary Policy in Economic Fluctuations," CEPR Discussion Papers 12911, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:12911
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    References listed on IDEAS

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    Cited by:

    1. Martin Geiger & Jochen Güntner, 2019. "How are oil supply shocks transmitted to the U.S. economy?," Economics working papers 2019-13, Department of Economics, Johannes Kepler University Linz, Austria.

    More about this item

    Keywords

    historical decompositions; impulse-response functions; informative priors; Model uncertainty; monetary policy; set identification; structural vector autoregressions;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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