Ruud P.A. An Introduction to Classical Econometric Theory

Oxford University Press, 2000. — 974 p. — ISBN: 0195111648, 9780195111644An 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.
Paul A. Ruud, Professor of Economics, University of California, BerkeleyКнига Рууда – наиболее удачная попытка создать учебник, базирующийся на осовремененных принципах эконометрики. Он довольно сбалансирован в подборе тем и материала, включает и эмпирические примеры, и геометрическую интерпретацию, и строго сформулированные определения и теоремы, и краткое резюме каждой главы, и методологические замечания, и упражнения. Нелинейные модели преобладают как наиболее общие, а метод максимального правдоподобия и обобщенный метод моментов занимают центральное место в качестве инструментария.
У учебника Рууда есть сайт. На сайте, в частности, имеются данные и программы на языке MatLAB.The Least-Squares Linear Fit
The Geometry of Least Squares
Partitioned Fit
Restricted Least Squares
Overview of Ordinary Least Squares
Linear Unbiased Estimation
Variances and Covariances
Variances and Covariances of Ordinary Least Squares
Efficient Estimation
Normal Distribution Theory
Hypothesis Testing
Overview of Linear Regression
Nonnormal Disbribution Theory
Maximum Likelihood Estimation
Maximum Likelihood Asymptotic Distribution Theory
Maximul Likelihood Computation
Maximum Likelihood Statistical Inference
Heteroskedasticity
Serial Correlation
Instrumental Variables Estimation
The Generalized Method of Moments
Generalized Method of Moments Hypothesis Tests
Overview
Panel Data Models
Autoregressive Moving-Average Time Series Models
Simultaneous Equations
Discrete Dependent Variables
Censored and Truncated Variables
Overview
Appendices