Bayesian Local Projections

Silvia Miranda-Agrippino and Giovanni Ricco ()

The Warwick Economics Research Paper Series (TWERPS) from University of Warwick, Department of Economics

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.

Keywords: Local Projections; VARs JEL Classification: C11; C14 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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https://warwick.ac.uk/fac/soc/economics/research/w ... erp_1348_-_ricco.pdf

Related works:
Working Paper: Bayesian Local Projections (2023)
Working Paper: Bayesian Local Projections (2023)
Working Paper: Bayesian local projections (2021)
Working Paper: Bayesian local projections (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:wrk:warwec:1348

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