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A sequential Monte Carlo approach to inference in multiple‐equation Markov‐switching models

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  • Mark Bognanni
  • Edward Herbst

Abstract

Vector autoregressions with Markov‐switching parameters (MS‐VARs) offer substantial gains in data fit over VARs with constant parameters. However, Bayesian inference for MS‐VARs has remained challenging, impeding their uptake for empirical applications. We show that sequential Monte Carlo (SMC) estimators can accurately estimate MS‐VAR posteriors. Relative to multi‐step, model‐specific MCMC routines, SMC has the advantages of generality, parallelizability, and freedom from reliance on particular analytical relationships between prior and likelihood. We use SMC's flexibility to demonstrate that model selection among MS‐VARs can be highly sensitive to the choice of prior.

Suggested Citation

  • Mark Bognanni & Edward Herbst, 2018. "A sequential Monte Carlo approach to inference in multiple‐equation Markov‐switching models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 126-140, January.
    • Handle: RePEc:wly:japmet:v:33:y:2018:i:1:p:126-140
    DOI: 10.1002/jae.2582
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    Cited by:

    1. Hee Soo (test record) Kim & Christian Matthes & Toan Phan, 2011. "Extreme Weather and the Macroeconomy," Working Paper 21-14, Federal Reserve Bank of Richmond.
    2. Aruoba, S. Borağan & Mlikota, Marko & Schorfheide, Frank & Villalvazo, Sergio, 2022. "SVARs with occasionally-binding constraints," Journal of Econometrics, Elsevier, vol. 231(2), pages 477-499.
    3. Ettmeier, Stephanie & Kriwoluzky, Alexander, 2019. "Active, or passive? Revisiting the role of fiscal policy in the Great Inflation," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203609, Verein für Socialpolitik / German Economic Association.
    4. Hauzenberger Niko & Huber Florian & Pfarrhofer Michael & Zörner Thomas O., 2021. "Stochastic model specification in Markov switching vector error correction models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(2), pages 1-17, April.
    5. Kirstin Hubrich & Daniel F. Waggoner, 2022. "The Transmission of Financial Shocks and Leverage of Financial Institutions: An Endogenous Regime-Switching Framework," FRB Atlanta Working Paper 2022-5, Federal Reserve Bank of Atlanta.
    6. Stephanie Ettmeier & Alexander Kriwoluzky, 2020. "Active, or Passive? Revisiting the Role of Fiscal Policy in the Great Inflation," Discussion Papers of DIW Berlin 1872, DIW Berlin, German Institute for Economic Research.
    7. Herbst, Edward & Schorfheide, Frank, 2019. "Tempered particle filtering," Journal of Econometrics, Elsevier, vol. 210(1), pages 26-44.
    8. Böhl, Gregor, 2022. "Ensemble MCMC sampling for robust Bayesian inference," IMFS Working Paper Series 177, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    9. Ross Doppelt & Keith O'Hara, 2018. "Bayesian Estimation of Fractionally Integrated Vector Autoregressions and an Application to Identified Technology Shocks," 2018 Meeting Papers 1212, Society for Economic Dynamics.
    10. Stefano Grassi & Marco Lorusso & Francesco Ravazzolo, 2021. "Adaptive Importance Sampling for DSGE Models," BEMPS - Bozen Economics & Management Paper Series BEMPS84, Faculty of Economics and Management at the Free University of Bozen.
    11. Stephanie Ettmeier & Alexander Kriwoluzky, 2024. "Active or Passive? Revisiting the Role of Fiscal Policy During High Inflation," CRC TR 224 Discussion Paper Series crctr224_2024_565, University of Bonn and University of Mannheim, Germany.

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