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Measuring Contagion with a Bayesian Time-Varying Coefficient Model

Alessandro Rebucci () and Matteo Ciccarelli

No 03/171, IMF Working Papers from International Monetary Fund

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: International financial markets; contagion, Gibbs sampling, heteroskedasticity, omitted variable bias, time-varying coefficient models, correlation, covariance, parameter vector, correlations, Bayesian Analysis, Simulation Methods, Open Economy Macroeconomics, International Policy Coordination and Transmission, (search for similar items in EconPapers)
Date: 2003-09-01
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Related works:
Working Paper: Measuring contagion with a Bayesian, time-varying coefficient model (2003) Downloads
Working Paper: MEASURING CONTAGION WITH A BAYESIAN TIME-VARYING COEFFICIENT MODEL (2003) Downloads
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