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A sequential Monte Carlo approach to inference in multiple‐equation Markov‐switching models
Author(s) -
Bognanni Mark,
Herbst Edward
Publication year - 2017
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2582
Subject(s) - markov chain monte carlo , inference , computer science , estimator , monte carlo method , bayesian inference , bayesian probability , flexibility (engineering) , model selection , generality , econometrics , approximate bayesian computation , algorithm , mathematics , statistics , machine learning , artificial intelligence , economics , management
Summary 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.