An Introduction to Classical Econometric Theory

• Author is very well known and respected in his field.
• This text brings the results of a distinguished scholarly and teaching career to the development of a core textbook for graduate econometrics.
• The author makes sense of this complex field by presenting a careful intuitive understanding of the subject, not simply a set of methods for solving problems. He teaches students to think like econometricians, not just how to get "right" answers.
• Sound and logical coverage of content and careful pedagogical presentation, such as the use of empirical examples to open chapters.
• One set of appendices at the end of the book provides a ready reference on matters such as notation and a "refresher" course on basic mathematics. A second set of appendices extends concept coverage for those who wish to expand upon the text.

Paul A. Ruud, Professor of Economics, University of California, Berkeley

"It is an excellent text with very few weaknesses and I strongly recommend it." - R Blundell, University College London

"A useful book that provides all the necessary mathematical tools for graduate students to analyse data obtained from repeatable experiments." - Aslib Book Guide, Vol.65, Aug. 2000.

"Book gives a deep insight into the foundation of modern econometrics" - Dr Hannes Winner, University of Innsbruck

"Excellent exposition, great clarity; coherency" - Luc Bauwens, University of Lourain

"Very advanced textbook which covers all aspects of classical econometric theory" - Josef Forsterer, University of Linz

"Ruud's book is very original, innovative and clear" - Recensioni e Segnalazioni Bibliografiche

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