全球智库信息集成服务系统(G2TT): DP8894 Common Drifting Volatility in Large Bayesian VARs

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP8894
	DP8894 Common Drifting Volatility in Large Bayesian VARs
	Massimiliano Marcellino; Andrea Carriero; Todd Clark
发表日期	2012-03-12
出版年	2012
语种	英语
摘要	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.
主题	International Macroeconomics
关键词	Bayesian vars Forecasting Prior specification stochastic volatility
URL	https://cepr.org/publications/dp8894
来源智库	Centre for Economic Policy Research (United Kingdom)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/537755
推荐引用方式 GB/T 7714	Massimiliano Marcellino,Andrea Carriero,Todd Clark. DP8894 Common Drifting Volatility in Large Bayesian VARs. 2012.