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Bayesian model comparison for time‐varying parameter VARs with stochastic volatility
Author(s) -
Chan Joshua C. C.,
Eisenstat Eric
Publication year - 2018
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.2617
Subject(s) - stochastic volatility , deviance information criterion , econometrics , marginal likelihood , estimator , volatility (finance) , bayesian probability , bayesian vector autoregression , vector autoregression , deviance (statistics) , statistics , mathematics , bayesian inference , computer science
Summary We develop importance sampling methods for computing two popular Bayesian model comparison criteria, namely, the marginal likelihood and the deviance information criterion (DIC) for time‐varying parameter vector autoregressions (TVP‐VARs), where both the regression coefficients and volatilities are drifting over time. The proposed estimators are based on the integrated likelihood, which are substantially more reliable than alternatives. Using US data, we find overwhelming support for the TVP‐VAR with stochastic volatility compared to a conventional constant coefficients VAR with homoskedastic innovations. Most of the gains, however, appear to have come from allowing for stochastic volatility rather than time variation in the VAR coefficients or contemporaneous relationships. Indeed, according to both criteria, a constant coefficients VAR with stochastic volatility outperforms the more general model with time‐varying parameters.