Journal
JOURNAL OF APPLIED ECONOMETRICS
Volume 33, Issue 4, Pages 509-532Publisher
WILEY
DOI: 10.1002/jae.2617
Keywords
-
Categories
Funding
- Australian Research Council [DE150100795]
- Australian Research Council [DE150100795] Funding Source: Australian Research Council
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available