Bayesian model comparison for time-varying parameter VARs with stochastic volatility

Joshua Chan and Eric Eisenstat

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: We develop importance sampling methods for computing two popular Bayesian model comparison criteria, namely, the marginal likelihood and deviance information criterion (DIC) for TVP-VARs with stochastic volatility. The proposed estimators are based on the integrated likelihood, which are substantially more reliable than alternatives. Specifically, integrated likelihood evaluation is achieved by integrating out the time-varying parameters analytically, while the log-volatilities are integrated out numerically via importance sampling. Using US and Australian data, we find overwhelming support for the TVPVAR with stochastic volatility compared to a conventional constant coefficients VAR with homoscedastic 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 receives similar support as the more general model with time-varying parameters.

Keywords: Bayesian; state space; marginal likelihood; deviance information criterion; great moderation (search for similar items in EconPapers)
JEL-codes: C11 C52 E32 E52 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2015-08
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Related works:
Journal Article: Bayesian model comparison for time‐varying parameter VARs with stochastic volatility (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2015-32

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