Bayesian Inference in Dynamic Econometric Models

Paperback | January 6, 2000

byLuc Bauwens, Michel Lubrano, Jean-Francois Richard

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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 basedon 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 conditionalheteroskedastic 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.

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From the Publisher

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 basedon simulations (such as Markov Chain Monte ...

Luc Bauwens is currently Professor of Economics at the Universite 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 Facultes universitaires catholique...

other books by Luc Bauwens

Format:PaperbackPublished:January 6, 2000Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0198773137

ISBN - 13:9780198773139

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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

Editorial Reviews

`presents a comprehensive review of dynamic econometric models from a Bayesian perspective ... four insightful introductory chapters ... provide a valuable synthesis of current ideas and their application to parameter estimation.'Paul Goodwin, International Journa of Forecasting, 2000