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Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach

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Abstract

Vector autoregressions with Markov-switching parameters (MS-VARs) offer dramatically better data fit than their constant-parameter predecessors. However, computational complications, as well as negative results about the importance of switching in parameters other than shock variances, have caused MS-VARs to see only sparse usage. For our first contribution, we document the effectiveness of Sequential Monte Carlo (SMC) algorithms at estimating MSVAR posteriors. Relative to multi-step, model-specific MCMC routines, SMC has the advantages of being simpler to implement, readily parallelizable, and unconstrained by reliance on convenient relationships between prior and likelihood. For our second contribution, we exploit SMC?s flexibility to demonstrate that the use of priors with superior data fit alters inference about the presence of time variation in macroeconomic dynamics. Using the same data as Sims, Waggoner, and Zha (2008, we provide evidence of recurrent episodes characterized by a flat Phillips Curve.

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  • Mark Bognanni & Edward P. Herbst, 2014. "Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach," Working Papers (Old Series) 1427, Federal Reserve Bank of Cleveland.
    • Handle: RePEc:fip:fedcwp:1427
    DOI: 10.26509/frbc-wp-201427
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    Cited by:

    1. Pierre Guérin & Danilo Leiva-Leon, 2017. "Monetary policy, stock market and sectoral comovement," Working Papers 1731, Banco de España.
    2. Herbst, Edward & Schorfheide, Frank, 2019. "Tempered particle filtering," Journal of Econometrics, Elsevier, vol. 210(1), pages 26-44.
    3. Dario Caldara & Edward Herbst, 2019. "Monetary Policy, Real Activity, and Credit Spreads: Evidence from Bayesian Proxy SVARs," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(1), pages 157-192, January.
    4. Daniel F. Waggoner & Hongwei Wu & Tao Zha, 2014. "The Dynamic Striated Metropolis-Hastings Sampler for High-Dimensional Models," FRB Atlanta Working Paper 2014-21, Federal Reserve Bank of Atlanta.

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    More about this item

    Keywords

    Vector Autoregressions; Sequential Monte Carlo; Regime-Switching Models; Bayesian Analysis;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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