Common Drifting Volatility in Large Bayesian VARs
Andrea Carriero (),
Todd Clark and
No 8894, CEPR Discussion Papers from C.E.P.R. Discussion Papers
The estimation of large Vector Autoregressions with stochastic volatility using standard methods is computationally very demanding. In this paper we propose to model conditional volatilities as driven by a single common unobserved factor. This is justified by the observation that the pattern of estimated volatilities in empirical analyses is often very similar across variables. Using a combination of a standard natural conjugate prior for the VAR coefficients, and an independent prior on a common stochastic volatility factor, we derive the posterior densities for the parameters of the resulting BVAR with common stochastic volatility (BVAR-CSV). Under the chosen prior the conditional posterior of the VAR coefficients features a Kroneker structure that allows for fast estimation, even in a large system. Using US and UK data, we show that, compared to a model with constant volatilities, our proposed common volatility model significantly improves model fit and forecast accuracy. The gains are comparable to or as great as the gains achieved with a conventional stochastic volatility specification that allows independent volatility processes for each variable. But our common volatility specification greatly speeds computations.
Keywords: Bayesian VARs; forecasting; prior specification; stochastic volatility (search for similar items in EconPapers)
JEL-codes: C11 C13 C33 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Journal Article: Common Drifting Volatility in Large Bayesian VARs (2016)
Working Paper: Common Drifting Volatility in Large Bayesian VARs (2012)
Working Paper: Common drifting volatility in large Bayesian VARs (2012)
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