全球智库信息集成服务系统(G2TT): DP16346 Using Time-Varying Volatility for Identification in Vector Autoregressions: An Application to Endogenous Uncertainty

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP16346
	DP16346 Using Time-Varying Volatility for Identification in Vector Autoregressions: An Application to Endogenous Uncertainty
	Massimiliano Marcellino; Andrea Carriero; Todd Clark
发表日期	2021-07-08
出版年	2021
语种	英语
摘要	We develop a structural vector autoregression with stochastic volatility in which one of the variables can impact both the mean and the variance of the other variables. We provide conditional posterior distributions for this model, develop an MCMC algorithm for estimation, and show how stochastic volatility can be used to provide useful restrictions for the identification of structural shocks. We then use the model with US data to show that some variables have a significant contemporaneous feedback effect on macroeconomic uncertainty, and overlooking this channel can lead to distortions in the estimated effects of uncertainty on the economy.
主题	Monetary Economics and Fluctuations
关键词	Endogeneity Causality stochastic volatility Bayesian methods
URL	https://cepr.org/publications/dp16346
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/545310
推荐引用方式 GB/T 7714	Massimiliano Marcellino,Andrea Carriero,Todd Clark. DP16346 Using Time-Varying Volatility for Identification in Vector Autoregressions: An Application to Endogenous Uncertainty. 2021.