A sequential Monte Carlo approach to inference in multiple‐equation Markov‐switching models
| Autor: | Edward P. Herbst, Mark Bognanni |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Flexibility (engineering)
Economics and Econometrics Markov chain Computer science Model selection 05 social sciences Inference Estimator Markov chain Monte Carlo Bayesian inference computer.software_genre symbols.namesake 0502 economics and business symbols Data mining 050207 economics Particle filter computer Algorithm Social Sciences (miscellaneous) 050205 econometrics |
| Zdroj: | Journal of Applied Econometrics. 33:126-140 |
| ISSN: | 1099-1255 0883-7252 |
| DOI: | 10.1002/jae.2582 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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