Measuring Contagion with a Bayesian Time-Varying Coefficient Model

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.

Number of Pages in PDF File: 33

Keywords: contagion Gibbs sampling heteroskedasticity omitted variable bias time-varying coefficient models

JEL Classification: C11 C15 F41 F42 G15

working papers series