[分享]一本好书的gauss代码（BAYESIAN INFERENCE IN DYNAMIC ECONOMETRIC MODELS）

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Bayesian Inference in Dynamic Econometric Models Luc Bauwens, Michel Lubrano, and Jean-François Richard [url=http://ukcatalogue.oup.com.libproxy.lib.unc.edu/category/academic/series/economics/ate.do]Advanced Texts in Econometrics[/url] 366 pages | graphs | 234x156mm 978-0-19-877313-9 | Paperback | 06 January 2000 Also available as: Hardback | eBook Price: £42.00    This item is printed to order. Items which are printed to order are normally despatched and charged within 5-10 days. Add to Wish List 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 Comprehensive, up-to-date coverage of the field Includes extensive examples of economic applications 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. Readership: Econometrics and statistics postgraduates. Professors and researchers in economics departments, business schools, statistics departments, or any research centre in the same fields, especially econometricians. The specification in this catalogue, including without limitation price, format, extent, number of illustrations, and month of publication, was as accurate as possible at the time the catalogue was compiled. Occasionally, due to the nature of some contractual restrictions, we are unable to ship a specific product to a particular territory. Jacket images are provisional and liable to change before publication.

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Bayesian Inference in Dynamic Econometric Models (Advanced Texts in Econometrics) [Paperback] Luc Bauwens   
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(Author), Michel Lubrano (Author), Jean-François Richard (Author)
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Front Matter









1 Decision Theory and Bayesian Inference


2 Bayesian Statistics and Linear Regression


3 Methods of Numerical Integration


4 Prior Densities for the Regression Model


5 Dynamic Regression Models


6 Unit Root Inference


7 Heteroscedasticity and ARCH


8 Non-Linear Time Series Models


9 Systems of Equations


End Matter