G2TT
来源类型Discussion paper
规范类型论文
来源IDDP16346
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
URLhttps://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.
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