Measuring contagion with a Bayesian; time-varying coefficient model

Matteo Ciccarelli and Alessandro Rebucci ()

No 263, Working Paper Series from European Central Bank

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. JEL Classification: C11; C15; F41; F42; G15.

Keywords: Contagion; Gibbs sampling; heteroskedasticity; omitted variable bias; time-varying coefficient models. (search for similar items in EconPapers)
Date: 2003-09
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Related works:
Working Paper: Measuring Contagion with a Bayesian Time-Varying Coefficient Model (2003)
Working Paper: MEASURING CONTAGION WITH A BAYESIAN TIME-VARYING COEFFICIENT MODEL (2003)
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