Bayesian inference in dynamic econometric models
著者
書誌事項
Bayesian inference in dynamic econometric models
（Advanced texts in econometrics）
Oxford University Press, 1999
- : hbk
- : pbk
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注記
Includes bibliographical references (p. [323]-339) and indexes
内容説明・目次
内容説明
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.
目次
- 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
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