Bayesian Inference in Dynamic Econometric Models

Name: Bayesian Inference in Dynamic Econometric Models
SKU: 2394875
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Advanced Texts in Econometrics

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Description

This book contains an up-to-date coverage of the last twenty years 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 regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods.

Author Biography:

Luc Bauwens is currently Professor of Economics at the Université catholique de Louvain, where he has been co-director of the Center for Operations Research and Econometrics (CORE) from 1992 to 1998. He has previously been a lecturer at Ecole des Hautes Etudes en Sciences Sociales (EHESS), France, at Facultés universitaires catholiques de Mons (FUCAM), Belgium, and a consultant at the World Bank, Washington DC. His research interests cover Bayesian inference, time series methods, simulation and numerical methods in econometrics, as well as empirical finance and international trade. Michel Lubrano is Directeur de Recherche at CNRS, part of GREQAM in Marseille. Jean-François Richard is University Professor of Economics at the University of Pittsburgh.

Release date Australia

January 6th, 2000

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Audience

Professional & Vocational

Illustrations

graphs

Pages

366

Series

Advanced Texts in Econometrics

Dimensions

155x235x20

ISBN-13

9780198773139

Product ID

2394875

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