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

Mark Bognanni and Edward Herbst

No 1427, Working Papers (Old Series) from Federal Reserve Bank of Cleveland

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.

Keywords: Vector Autoregressions; Sequential Monte Carlo; Regime-Switching Models; Bayesian Analysis (search for similar items in EconPapers)
JEL-codes: C11 C15 C32 C52 E3 E4 E5 (search for similar items in EconPapers)
Pages: 50 pages
Date: 2014-11-12
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Working Paper: Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach (2015)
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DOI: 10.26509/frbc-wp-201427

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