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Bayesian Inference in Dynamic Econometric Models

Bayesian Inference in Dynamic Econometric Models - Advanced Texts in Econometrics

Paperback (06 Jan 2000)

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Publisher's 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.

About the Publisher

Oxford University Press

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Our products cover an extremely broad academic and educational spectrum, and we aim to make our content available to our users in whichever format suits them best.We publish for all audiences-from pre-school to secondary level schoolchildren; students to academics; general readers to researchers; individuals to institutions. Our range includes dictionaries, English language teaching materials, children's books, journals, scholarly monographs, printed music, higher education textbooks, and schoolbooks.

Book information

ISBN: 9780198773139
Publisher: OUP OXFORD
Imprint: Oxford University Press
Pub date:
DEWEY: 330.01519542
DEWEY edition: 21
Language: English
Number of pages: 350
Weight: 514g
Height: 234mm
Width: 157mm
Spine width: 21mm