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Bayesian Local Projections

Author

Listed:
  • Miranda-Agrippino, Silvia

    (Bank of England, CfM and CEPR)

  • Ricco, Giovanni

    (University of Warwick, OFCE-Sciences Po and CEPR)

Abstract

We propose a Bayesian approach to Local Projections that optimally addresses the empirical bias-variance tradeo inherent in the choice between VARs and LPs. Bayesian Local Projections (BLP) regularise the LP regression models by using informative priors, thus estimating impulse response functions potentially better able to capture the properties of the data as compared to iterative VARs. In doing so, BLP preserve the exibility of LPs to empirical model misspeci cations while retaining a degree of estimation uncertainty comparable to a Bayesian VAR with standard macroeconomic priors. As a regularised direct forecast, this framework is also a valuable alternative to BVARs for multivariate out-of-sample projections.

Suggested Citation

  • Miranda-Agrippino, Silvia & Ricco, Giovanni, 2021. "Bayesian Local Projections," The Warwick Economics Research Paper Series (TWERPS) 1348, University of Warwick, Department of Economics.
    • Handle: RePEc:wrk:warwec:1348
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    References listed on IDEAS

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    More about this item

    Keywords

    Local Projections ; VARs JEL Classification: C11 ; C14;
    All these keywords.

    JEL classification:

    • 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
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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