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

Mark Bognanni and Edward Herbst

No 2015-116, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: Vector autoregressions with Markov-switching parameters (MS-VARs) fit the data better than do their constant-parameter predecessors. However, Bayesian inference for MS-VARs with existing algorithms remains challenging. For our first contribution, we show that Sequential Monte Carlo (SMC) estimators accurately estimate Bayesian 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. For our second contribution, we use SMC's flexibility to demonstrate that the choice of prior drives the key empirical finding of Sims, Waggoner, and Zha (2008) as much as does the data.

Keywords: Bayesian Analysis; Regime-Switching Models; Sequential Monte Carlo; Vector Autoregressions (search for similar items in EconPapers)
JEL-codes: C11 C18 C32 C52 E3 E4 E5 (search for similar items in EconPapers)
Pages: 56 pages
Date: 2015-12-18
New Economics Papers: this item is included in nep-ets, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2015-116

DOI: 10.17016/FEDS.2015.116

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