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Bayesian local projections

Abstract : We propose a Bayesian approach to Local Projections that optimally addresses the empirical bias-variance tradeoff 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 flexibility of LPs to empirical model misspecification 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.
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Preprints, Working Papers, ...
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Contributor : élisabeth Wolff-Maussion Connect in order to contact the contributor
Submitted on : Monday, October 11, 2021 - 3:26:31 PM
Last modification on : Monday, March 21, 2022 - 2:48:14 PM
Long-term archiving on: : Wednesday, January 12, 2022 - 8:09:32 PM


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  • HAL Id : hal-03373574, version 1



Silvia Miranda-Agrippino, Giovanni Ricco. Bayesian local projections. 2021. ⟨hal-03373574⟩



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