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

56 Pages Posted: 22 Dec 2015

See all articles by Mark Bognanni

Mark Bognanni

Board of Governors of the Federal Reserve System

Edward Herbst

Board of Governors of the Federal Reserve System

Multiple version iconThere are 2 versions of this paper

Date Written: December, 2015

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.

JEL Classification: C11, C18, C32, C52, E3, E4, E5

Suggested Citation

Bognanni, Mark and Herbst, Edward, Estimating (Markov-Switching) VAR Models Without Gibbs Sampling: A Sequential Monte Carlo Approach (December, 2015). Available at SSRN: https://ssrn.com/abstract=2705735 or http://dx.doi.org/10.17016/FEDS.2015.116

Mark Bognanni (Contact Author)

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

Edward Herbst

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

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