An Introduction to Classical Econometric Theory

This book is designed to fill the gap between introductory undergraduate texts and advanced texts for graduate students. Its comprehensive coverage ensures that readers understand both the 'how' and the 'why' of econometrics, as it explains not only the mathematical techniques for econometric problem-solving but also the mathematical foundations of the discipline. Developed with careful pedagogical methodology throughout, the text makes full use of empirical examples and includes appendices providing 'ready reference' and refresher courses on basic mathematics, as well as further material for the more advanced student.

Table of Contents

1. The Least-Squares Linear Fit; 2. The Geometry of Least Squares; 3. Partitioned Fit; 4. Restricted Least Squares; 5. Overview of Ordinary Least Squares; 6. Linear Unbiased Estimation; 7. Variances and Covariances; 8. Variances and Covariances of Ordinary Least Squares; 9. Efficient Estimation; 10. Normal Distribution Theory; 11. Hypothesis Testing; 12. Overview of Linear Regression; 13. Nonnormal Disbribution Theory; 14. Maximum Likelihood Estimation; 15. Maximum Likelihood Asymptotic Distribution Theory; 16. Maximul Likelihood Computation; 17. Maximum Likelihood Statistical Inference; 18. Heteroskedasticity; 19. Serial Correlation; 20. Instrumental Variables Estimation; 21. The Generalized Method of Moments; 22. Generalized Method of Moments Hypothesis Tests; 23. Overview; 24. Panel Data Models; 25. Autoregressive Moving-Average Time Series Models; 26. Simultaneous Equations; 27. Discrete Dependent Variables; 28. Censored and Truncated Variables; 29. Overview; APPENDICES; BIBLIOGRAPHY; INDEX

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From The Publisher:

In An Introduction to Classical Econometric Theory Paul A. Ruud shows the practical value of an intuitive approach to econometrics. Students learn not only why but how things work. Through geometry, seemingly distinct ideas are presented as the result of one common principle, making
econometrics more than mere recipes or special tricks. In doing this, the author relies on such concepts as the linear vector space, orthogonality, and distance. Parts I and II introduce the ordinary least squares fitting method and the classical linear regression model, separately rather than
simultaneously as in other texts. Part III contains generalizations of the classical linear regression model and Part IV develops the latent variable models that distinguish econometrics from statistics. To motivate formal results in a chapter, the author begins with substantive empirical examples.
Main results are followed by illustrative special cases; technical proofs appear toward the end of each chapter. Intended for a graduate audience, An Introduction to Classical Econometric Theory fills the gap between introductory and more advanced texts. It is the most conceptually complete text for
graduate econometrics courses and will play a vital role in graduate instruction.