Measuring Contagion with a Bayesian, Time-Varying Coefficient Model

45 Pages Posted: 26 Jan 2004

See all articles by Matteo Ciccarelli

Matteo Ciccarelli

European Central Bank (ECB)

Alessandro Rebucci

Johns Hopkins University - Carey Business School; National Bureau of Economic Research (NBER); Centre for Economic Policy Research (CEPR)

Multiple version iconThere are 3 versions of this paper

Date Written: September 2003

Abstract

To measure contagion empirically, we propose using a Bayesian time-varying coefficient model estimated with Markov Chain Monte Carlo methods. The proposed measure works in the joint presence of heteroskedasticity and omitted variables and does not require knowledge of the timing of the crisis. It distinguishes contagion not only from interdependence but also from structural breaks. It can be used to investigate positive as well as negative contagion. The proposed measure appears to work well using both simulated and actual data.

Keywords: Contagion, Gibbs sampling, Heteroskedasticity, Omitted variable bias, Time-varying coefficient models

JEL Classification: C11, C15, F41, F42, G15

Suggested Citation

Ciccarelli, Matteo and Rebucci, Alessandro, Measuring Contagion with a Bayesian, Time-Varying Coefficient Model (September 2003). Available at SSRN: https://ssrn.com/abstract=457531

Matteo Ciccarelli (Contact Author)

European Central Bank (ECB) ( email )

Sonnemannstrasse 22
Frankfurt am Main, 60314
Germany

Alessandro Rebucci

Johns Hopkins University - Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States

HOME PAGE: http://carey.jhu.edu/faculty-research/faculty-directory/alessandro-rebucci-phd

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

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