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Uniform Priors for Impulse Responses

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Abstract

There has been a call for caution when using the conventional method for Bayesian inference in set-identified structural vector autoregressions on the grounds that the uniform prior over the set of orthogonal matrices could be nonuniform for key objects of interest. This paper challenges this call. Although the prior distributions of individual impulse responses induced by the conventional method may be nonuniform, they typically do not drive the posteriors if one does not condition on the reduced-form parameters. Importantly, when the focus is on joint inference, the uniform prior over the set of orthogonal matrices is not only sufficient but also necessary for inference based on a uniform joint prior distribution over the identified set for the vector of impulse responses. We also propose variants of the conventional method to conduct inference based on a uniform joint prior distribution for the vector of impulse responses. We generalize our results to vectors of objects of interest beyond impulse responses.

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  • Jonas E. Arias & Juan F. Rubio-Ramirez & Daniel F. Waggoner, 2020. "Uniform Priors for Impulse Responses," Working Papers 22-30, Federal Reserve Bank of Philadelphia.
  • Handle: RePEc:fip:fedpwp:94737
    DOI: 10.21799/frbp.wp.2022.30
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    Cited by:

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    2. Gökhan Ider & Alexander Kriwoluzky & Frederik Kurcz & Ben Schumann, 2024. "Friend, Not Foe - Energy Prices and European Monetary Policy," Discussion Papers of DIW Berlin 2089, DIW Berlin, German Institute for Economic Research.

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    More about this item

    Keywords

    Structural vector autoregressions; priors; posteriors; impulse responses; joint inference.;
    All these keywords.

    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

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