Bayesian Inference in Dynamic Econometric Models / Edition 1

by Luc Bauwens, Michel Lubrano, Jean-François Richard

Overview

This book offers an up-to-date coverage of the basic principles and tools of 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 , and the long available analytical results of Bayesian inference for linear regression models.

About the Series
Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.

Product Details

ISBN-13:	9780198773139
Publisher:	Oxford University Press
Publication date:	03/23/2000
Series:	Advanced Texts in Econometrics
Edition description:	New Edition
Pages:	366
Product dimensions:	6.10(w) x 9.10(h) x 0.80(d)

About the Author

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.

Table of Contents

Chapter 1: Decision Theory and Bayesian InferenceChapter 2: Bayesian Statistics and Linear RegressionChapter 3: Methods of Numerical IntegrationChapter 4: Prior Densities for the Regression ModelChapter 5: Dynamic Regression ModelsChapter 6: Bayesian Unit RootsChapter 7: Heteroskedasticity and ARCHChapter 8: Nonlinear Tome Series ModelsChapter 9: Systems of EquationsAppendix A: Probability DistributionsAppendix B: Generating Random Numbers