Using Time-Varying Volatility for Identification in Vector Autoregressions: An Application to Endogenous Uncertainty

Massimiliano Marcellino, Andrea Carriero and Todd Clark

No 16346, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: 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.

Keywords: Endogeneity; Causality; stochastic volatility; Bayesian methods (search for similar items in EconPapers)
JEL-codes: C11 C32 D81 E32 (search for similar items in EconPapers)
Date: 2021-07
References: Add references at CitEc
Citations:

Downloads: (external link)
https://cepr.org/publications/DP16346 (application/pdf)
CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org

Related works:
Journal Article: Using time-varying volatility for identification in Vector Autoregressions: An application to endogenous uncertainty (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:16346

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP16346

Access Statistics for this paper

More papers in CEPR Discussion Papers from C.E.P.R. Discussion Papers Centre for Economic Policy Research, 33 Great Sutton Street, London EC1V 0DX.
Bibliographic data for series maintained by ().