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Measuring contagion with a Bayesian; time-varying coefficient model Author info | Abstract | Publisher info | Download info | Related research | Statistics Matteo Ciccarelli () (European Central Bank, DG Research, Kaiserstrasse 29, D-60311, Frankfurt am Main, Germany. )
Alessandro Rebucci () (International Monetary Fund, Research Department, 700 19th St., N.W., Washington, D.C. 20431, USA. )
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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.
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Paper provided by European Central Bank in its series Working Paper Series with number
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Length: 45 pages
Date of creation: Sep 2003Date of revision:
Handle: RePEc:ecb:ecbwps:20030263Contact details of provider: Postal: Postfach 16 03 19, Frankfurt am Main, Germany Phone: +49 69 1344 0 Fax: +49 69 1344 6000 Web page: http://www.ecb.europa.eu/home/html/index.en.html More information through EDIRC
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Keywords: Contagion ; Gibbs sampling ; heteroskedasticity ; omitted variable bias ; time-varying coefficient models. ; Other versions of this item:
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.: Favero, Carlo A. & Giavazzi, Francesco, 2002.
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Juan Francisco Rubio-Ramírez & Daniel Waggoner & Tao Zha, 2005.
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