Measuring Contagion with a Bayesian Time-Varying Coefficient Model

33 Pages Posted: 3 Feb 2006

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

We propose using a Bayesian time-varying coefficient model estimated with Markov chain-Monte Carlo methods to measure contagion empirically. 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 and 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). IMF Working Paper No. 03/171, Available at SSRN: https://ssrn.com/abstract=880216

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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