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Estimation and inference in econometrics. (English) Zbl 1009.62596
New York, NY: Oxford University Press. xx, 874 p. (1993).
From the preface: Readers are expected to have some acquaintance with elementary econometrics before starting, but no more than would be part of a typical undergraduate curriculum. They are also expected to have the mathematical maturity appropriate to graduate students in economics, although we do provide two appendices that cover the mathematical and statistical prerequisites for understanding the material.
Contents: 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; Appendices: A. Matrix algebra; B. Results from probability theory.

62P20 Applications of statistics to economics
62-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics
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