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Item Details
| Title:
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BAYESIAN INFERENCE IN DYNAMIC ECONOMETRIC MODELS
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| By: |
Luc Bauwens, etc., Michel Lubrano |
| Format: |
Hardback |
| List price:
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£165.00 |
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We currently do not stock this item, please contact the publisher directly for
further information.
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| ISBN 10: |
0198773129 |
| ISBN 13: |
9780198773122 |
| Publisher: |
OXFORD UNIVERSITY PRESS |
| Pub. date: |
6 January, 2000 |
| Series: |
Advanced Texts in Econometrics |
| Pages: |
366 |
| Description: |
This book offers an up-to-date coverage of the basic principles and of the tools of Bayesian inference in econometrics. Bayesian inference is a branch of statistics that integrates explicitly both data and prior (possibly subjective) information in model building, estimation, and evaluation. The book then shows how to use Bayesian methods in a range of models especially suited to the analysis of macroeconomic and financial time series. |
| 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. |
| Illustrations: |
graphs |
| Publication: |
UK |
| Imprint: |
Oxford University Press |
| Returns: |
Non-returnable |
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