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Bayesian Local Projections

Author

Listed:
  • Leonardo N. Ferreira

    (Central Bank of Brazil)

  • Silvia Miranda-Agrippino

    (Federal Reserve Bank of New York and CEPR)

  • Giovanni Ricco

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

Abstract

We propose a Bayesian approach to Local Projections (LPs) that optimally addresses the empirical bias-variance trade-off intrinsic in the choice between direct and iterative methods. Bayesian Local Projections (BLPs) regularize LP regressions via informative priors and estimate impulse response functions that capture the properties of the data more accurately than iterative VARs. BLPs preserve the flexibility of LPs while retaining a degree of estimation uncertainty comparable to Bayesian VARs with standard macroeconomic priors. As regularized direct forecasts, BLPs are also a valuable alternative to BVARs for multivariate out-of-sample projections.

Suggested Citation

  • Leonardo N. Ferreira & Silvia Miranda-Agrippino & Giovanni Ricco, 2025. "Bayesian Local Projections," The Review of Economics and Statistics, MIT Press, vol. 107(5), pages 1424-1438, September.
    • Handle: RePEc:tpr:restat:v:107:y:2025:i:5:p:1424-1438
    DOI: 10.1162/rest_a_01334
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    2. Le, Anh H., 2025. "Climate change and Carbon policy: A story of optimal green macroprudential and capital flow management," Energy Economics, Elsevier, vol. 146(C).
    3. Viktoriia Bannikova & Sofya Kolesnik, 2025. "Assessing the predictability of market interest rate changes on Central Bank of Russia press release days," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 77, pages 25-45.
    4. Li, Dake & Plagborg-Møller, Mikkel & Wolf, Christian K., 2024. "Local projections vs. VARs: Lessons from thousands of DGPs," Journal of Econometrics, Elsevier, vol. 244(2).
    5. Aleksei Nemtyrev & Otilia Boldea, 2026. "Targeted Local Projections," Papers 2603.00248, arXiv.org.
    6. Ho, Paul & Lubik, Thomas A. & Matthes, Christian, 2024. "Averaging impulse responses using prediction pools," Journal of Monetary Economics, Elsevier, vol. 146(C).
    7. S. Borağan Aruoba & Thomas Drechsel, 2024. "The Long and Variable Lags of Monetary Policy: Evidence from Disaggregated Price Indices," NBER Chapters, in: Inflation in the COVID Era and Beyond, National Bureau of Economic Research, Inc.
    8. NAKAJIMA, Jouchi, 2025. "Time-varying Local Projections with Stochastic Volatility," Discussion Paper Series 761, Institute of Economic Research, Hitotsubashi University.
    9. Tomás Opazo, 2023. "The Heterogeneous Effect of Monetary Policy Shocks: Evidence for US Households," Working Papers Central Bank of Chile 992, Central Bank of Chile.
    10. Bernardini, Marco & De Nicola, Annalisa, 2025. "The market stabilization role of central bank asset purchases: High-frequency evidence from the COVID-19 crisis," Journal of International Money and Finance, Elsevier, vol. 152(C).
    11. Atsushi Inoue & Òscar Jordà & Guido M Kuersteiner, 2026. "Inference for local projections," The Econometrics Journal, Royal Economic Society, vol. 29(1), pages 2-26.

    More about this item

    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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