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Bayesian Inference in Dynamic Econometric Models$
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Luc Bauwens, Michel Lubrano, and Jean-François Richard

Print publication date: 2000

Print ISBN-13: 9780198773122

Published to Oxford Scholarship Online: September 2011

DOI: 10.1093/acprof:oso/9780198773122.001.0001

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Heteroscedasticity and ARCH

Heteroscedasticity and ARCH

Chapter:
(p.197) 7 Heteroscedasticity and ARCH
Source:
Bayesian Inference in Dynamic Econometric Models
Author(s):

Luc Bauwens

Michel Lubrano

Jean-François Richard

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198773122.003.0007

This chapter examines the importance of heteroscedasticity and the autoregressive conditional heteroscedasticity (ARCH) model in econometric analysis, particularly in the Bayesian inference approach. It discusses the case of functional heteroscedasticity and proposes a general method for detecting heteroscedasticity. It explains that neglecting heteroscedasticity may result in a posterior distribution for the regression coefficients which is different from what it is when the heteroscedasticity is taken into account.

Keywords:   heteroscedasticity, ARCH model, econometric analysis, Bayesian inference, regression coefficients

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