Hardcover. 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 abroad range of rather recent models for economic time series, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It containsalso an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods. This book covers the principles and tools of Bayesian inference in econometrics. Bayesian inference is a branch of statistics that integrates explicitly both data and prior information in model building, estimation and evaluation. The book shows how to use Bayesian methods in models suited to the analysis of macroeconomic and financial time series Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Bayesian Inference in Dynamic Econometric Models (Hardcover)
Editor: Oxford University Press, Oxford
Fecha de publicaci�n: 2000
Idioma: Ingl�s
ISBN 10: 0198773129
ISBN 13: 9780198773122
Encuadernaci�n: Hardcover
Estado: new

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Sinopsis

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.

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Luc Bauwens is currently Professor of Economics at the Universit� 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 Facult�s 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-Fran�ois Richard is University Professor of Economics at the University of Pittsburgh.

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