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Fast Posterior Sampling in Tightly Identified SVARs Using 'Soft' Sign Restrictions

Author

Listed:
  • Matthew Read

    (Reserve Bank of Australia)

  • Dan Zhu

    (Department of Econometrics and Business Statistics, Monash University)

Abstract

We propose algorithms for conducting Bayesian inference in structural vector autoregressions identified using sign restrictions. The key feature of our approach is a sampling step based on 'soft' sign restrictions. This step draws from a target density that smoothly penalises parameter values violating the restrictions, facilitating the use of computationally efficient Markov chain Monte Carlo sampling algorithms. An importance-sampling step yields draws from the desired distribution conditional on the 'hard' sign restrictions. Relative to standard accept-reject sampling, the method substantially improves computational efficiency when identification is 'tight'. It can also greatly reduce the computational burden of implementing prior-robust Bayesian methods. We illustrate the broad applicability of the approach in a model of the global oil market identified using a rich set of sign, elasticity and narrative restrictions.

Suggested Citation

  • Matthew Read & Dan Zhu, 2025. "Fast Posterior Sampling in Tightly Identified SVARs Using 'Soft' Sign Restrictions," RBA Research Discussion Papers rdp2025-03, Reserve Bank of Australia.
  • Handle: RePEc:rba:rbardp:rdp2025-03
    DOI: 10.47688/rdp2025-03
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    References listed on IDEAS

    as
    1. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    2. Christiane Baumeister & James D. Hamilton, 2019. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks," American Economic Review, American Economic Association, vol. 109(5), pages 1873-1910, May.
    3. Matthew Read, 2022. "Algorithms for inference in SVARs identified with sign and zero restrictions [Identification and inference with ranking restrictions]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 699-718.
    4. Lutz Kilian & Daniel P. Murphy, 2012. "Why Agnostic Sign Restrictions Are Not Enough: Understanding The Dynamics Of Oil Market Var Models," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1166-1188, October.
    5. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, Enero-Abr.
    6. Martin Bruns & Michele Piffer, 2023. "A new posterior sampler for Bayesian structural vector autoregressive models," Quantitative Economics, Econometric Society, vol. 14(4), pages 1221-1250, November.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Bayesian inference; Markov chain Monte Carlo; oil market; sign restrictions; structural vector autoregression;
    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
    • Q35 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Hydrocarbon Resources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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