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Bayesian Inference in Dynamic Econometric Models
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Bayesian Inference in Dynamic Econometric Models

Luc Bauwens, Michel Lubrano, and Jean-François Richard

Abstract

This book contains an up-to-date coverage of the last twenty years of advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non-linear models, by integrating the useful developments of numerical integration techniques based on simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a broad range of rather recent models for economic time series, such as non-linear models, autoregressive conditional heteroskedastic ... More

Keywords: Bayesian, econometrics, dynamic models, numerical integration, Markov Chain Monte Carlo methods, linear regression, non-linear models, autoregressive conditional heteroskedastic regressions, cointegrated vector autoregressive models, unit root inference

Bibliographic Information

Print publication date: 2000 Print ISBN-13: 9780198773122
Published to Oxford Scholarship Online: October 2011 DOI:10.1093/acprof:oso/9780198773122.001.0001

Authors

Affiliations are at time of print publication.

Luc Bauwens, author
Université Catholique de Louvain

Michel Lubrano, author
GREQAM, CNRS

Jean-François Richard, author
University of Pittsburgh

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