PDF版本  

注意：论坛之前有过一个版本，但是我这个比原来那个要更清晰，
https://bbs.pinggu.org/thread-631470-1-1.html 不信的话，各位可以比较一下

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.


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