Description: Bayesian Inference in Dynamic Econometric Models Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Author(s): Luc Bauwens, Michel Lubrano, Jean-Francois Richard Format: Hardback Publisher: Oxford University Press, United Kingdom Imprint: Oxford University Press ISBN-13: 9780198773122, 978-0198773122 Synopsis 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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Book Title: Bayesian Inference in Dynamic Econometric Models
Item Height: 242 mm
Item Width: 163 mm
Series: Advanced Texts in Econometrics
Author: Michel Lubrano, Jean-Francois Richard, Luc Bauwens
Publication Name: Bayesian Inference in Dynamic Econometric Models
Format: Hardcover
Language: English
Publisher: Oxford University Press
Subject: Economics, Computer Science, Mathematics
Publication Year: 2000
Type: Textbook
Item Weight: 673 g
Number of Pages: 366 Pages
