Bayesian Inference in Dynamic Econometric Models (Hardcover, New)

This work contains an up-to-date coverage of the last 20 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. This book is intended for econometrics and statistics postgraduates, professors and researchers in economics departments, business schools, statistics departments, or any research centre in the same fields, especially econometricians.

General

Imprint:	Oxford UniversityPress
Country of origin:	United Kingdom
Series:	Advanced Texts in Econometrics
Release date:	2000
First published:	March 2000
Authors:	Luc Bauwens (Professor of Economics, Centre for Operations Research and Econometrics [CORE]) • Michel Lubrano (Directeur de Recherche) • Jean-Francois Richard (University Professor of Economics)
Dimensions:	242 x 163 x 24mm (L x W x T)
Format:	Hardcover
Pages:	366
Edition:	New
ISBN-13:	978-0-19-877312-2
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > Applications of computing > Computer modelling & simulation Books > Business & Economics > Economics > Econometrics > General Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling Books > Computing & IT > Computer software packages > Computer graphics software > General
LSN:	0-19-877312-9
Barcode:	9780198773122

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