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Inference in Bayesian Proxy-SVARs

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Abstract

Motivated by the increasing use of external instruments to identify structural vector autoregressions SVARs), we develop algorithms for exact finite sample inference in this class of time series models, commonly known as proxy SVARs. Our algorithms make independent draws from the normal-generalized-normal family of conjugate posterior distributions over the structural parameterization of a proxy-SVAR. Importantly, our techniques can handle the case of set identification and hence they can be used to relax the additional exclusion restrictions unrelated to the external instruments often imposed to facilitate inference when more than one instrument is used to identify more than one equation as in Mertens and Montiel-Olea (2018).

Suggested Citation

  • Jonas E. Arias & Juan F. Rubio-Ramirez & Daniel F. Waggoner, 2018. "Inference in Bayesian Proxy-SVARs," Working Papers 18-25/R, Federal Reserve Bank of Philadelphia.
    • Handle: RePEc:fip:fedpwp:18-25
    DOI: 10.21799/frbp.wp.2018.25
    Note: Revised October 2020
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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: 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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