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Bayesian inference in dynamic econometric models

Author: Luc Bauwens; Michel Lubrano; Jean François Richard
Publisher: Oxford [England] ; New York : Oxford University Press, 2003.
Series: Advanced texts in econometrics.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
Offering an up-to-date coverage of the basic principles and tools of Bayesian inference in economics, this textbook then shows how to use Bayesian methods in a range of models suited to the analysis of macroeconomic and financial time series.
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Luc Bauwens; Michel Lubrano; Jean François Richard
ISBN: 0198773137 9780198773139 0198773129 9780198773122
OCLC Number: 704272054
Description: xv, 350 s. ; 24 cm.
Contents: Chapter 1: Decision Theory and Bayesian Inference ; Chapter 2: Bayesian Statistics and Linear Regression ; Chapter 3: Methods of Numerical Integration ; Chapter 4: Prior Densities for the Regression Model ; Chapter 5: Dynamic Regression Models ; Chapter 6: Bayesian Unit Roots ; Chapter 7: Heteroskedasticity and ARCH ; Chapter 8: Nonlinear Tome Series Models ; Chapter 9: Systems of Equations ; Appendix A: Probability Distributions ; Appendix B: Generating Random Numbers
Series Title: Advanced texts in econometrics.
Responsibility: Luc Bauwens, Michel Lubrano, and Jean-Francois Richard.
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Abstract:

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. Several examples illustrate the methods.  Read more...

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presents a comprehensive review of dynamic econometric models from a Bayesian perspective ... four insightful introductory chapters ... provide a valuable synthesis of current ideas and their Read more...

 
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