Estimation and inference in econometrics
著者
書誌事項
Estimation and inference in econometrics
Oxford University Press, 1993
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注記
Bibliography: p. 812-850
Includes indexes
内容説明・目次
内容説明
Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation.
One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package.
Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the
basic material and separate chapters on areas of specialized interest.
目次
- 1. The Geometry of Least Squares
- 2. Nonlinear Regression Models and Nonlinear Least Squares
- 3. Inference in Nonlinear Regression Models
- 4. Introduction to Asymptotic Theory and Methods
- 5. Asymptotic Methods and Nonlinear Least Squares
- 6. The Gauss-Newton Regression
- 7. Instrumental Variables
- 8. The Method of Maximum Likelihood
- 9. Maximum Likelihood and Generalized Least Squares
- 10. Serial Correlation
- 11. Tests Based on the Gauss-Newton Regression
- 12. Interpreting Tests in Regression Directions
- 13. The Classical Hypothesis Tests
- 14. Transforming the Dependent Variable
- 15. Qualitative and Limited Dependent Variables
- 16. Heteroskedasticity and Related Topics
- 17. The Generalized Method of Moments
- 18. Simultaneous Equations Models
- 19. Regression Models for Time-Series Data
- 20. Unit Roots and Cointegration
- 21. Monte Carlo Experiments
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