This two-volume work aims to present as completely as possible the methods of statistical inference with special reference to their economic applications. The reader will find a description not only of the classical concepts and results of mathematical statistics, but also of concepts and methods recently developed for the specific needs of econometrics. The authors have sought to avoid an overly technical presentation and go to some lengths to encourage an intuitive understanding of the results by providing numerous examples throughout. The breadth of approaches and the extensive coverage of the two volumes provide for a thorough and entirely self-contained course in modern econometrics. Volume 1 provides an introduction to general concepts and methods in statistics and econometrics, and goes on to cover estimation and prediction. Volume 2 focuses on testing, confidence regions, model selection, and asymptotic theory.

Table of Contents

1. Introduction to tests of hypotheses; 2. Uniformly most powerful tests; 3. Unbiased tests and invariant tests; 4. Likelihood based tests; 5. General asymptotic tests; 6. Multiple tests; 7. Set estimation and confidence regions; 8. Inequality constraints: estimation and testing; 9. Nonnested tests and model selection criteria; 10. Asymptotic efficiency; 11. Asymptotic theory; Appendix A. Review of linear algebra and matrix calculus; Appendix B. Review of probability.