Econometrics provides first-year graduate students with a thoroughly modern introduction to the subject, covering all the standard material necessary for understanding the principal techniques of econometrics, from ordinary least squares through cointegration. The book is distinctive in developing both time-series and cross-section analysis fully, giving readers a unified framework for understanding and integrating results.
Econometrics covers all the important topics in a succinct manner. All the estimation techniques that could possibly be taught in a first-year graduate course, except maximum likelihood, are treated as special cases of GMM (generalized methods of moments). Maximum likelihood estimators for a variety of models, such as probit and tobit, are collected in a separate chapter. This arrangement enables students to learn various estimation techniques in an efficient way. Virtually all the chapters include empirical applications drawn from labor economics, industrial organization, domestic and international finance, and macroeconomics. These empirical exercises provide students with hands-on experience applying the techniques covered. The exposition is rigorous yet accessible, requiring a working knowledge of very basic linear algebra and probability theory. All the results are stated as propositions so that students can see the points of the discussion and also the conditions under which those results hold. Most propositions are proved in the text.
For students who intend to write a thesis on applied topics, the empirical applications in Econometrics are an excellent way to learn how to conduct empirical research. For theoretically inclined students, the no-compromise treatment of basic techniques is an ideal preparation for more advanced theory courses.
Fumio Hayashi is a professor at the National Graduate Institute for Policy Studies in Tokyo. He has taught at the University of Tokyo, Columbia University, and the University of Pennsylvania. He is the author of Understanding Saving: Evidence from the United States and Japan.
“Students of econometrics and their teachers will find this book to be the best introduction to the subject at the graduate and advanced undergraduate level. Starting with least squares regression, Hayashi provides an elegant exposition of all the standard topics of econometrics, including a detailed discussion of stationary and nonstationary time series. The particular strength of the book is the excellent balance between econometric theory and its applications, using GMM as an organizing principle throughout. Each chapter includes a detailed empirical example taken from classic and current applications of econometrics.”—Dale Jorgensen, Harvard University
“Econometrics will be a very useful book for intermediate and advanced graduate courses. It covers the topics with an easy-to-understand approach while at the same time offering a rigorous analysis. The computer programming tips and problems should also be useful to students. I highly recommend this book for an up-to-date coverage and thoughtful discussion of topics in the methodology and application of econometrics.”—Jerry A. Hausman, Massachusetts Institute of Technology
“Econometrics covers both modern and classic topics without shifting gears. The coverage is quite advanced yet the presentation is simple. Hayashi brings students to the frontier of applied econometric practice through a careful and efficient discussion of modern economic theory. The empirical exercises are very useful. The projects are carefully crafted and have been thoroughly debugged.”—Mark W. Watson, Princeton University
“Econometrics strikes a good balance between technical rigor and clear exposition. The use of empirical examples is well done throughout. I very much like the use of old ‘classic’ examples. It gives students a sense of history—and shows that great empirical econometrics is a matter of having important ideas and good data, not just fancy new methods. The style is just great, informal and engaging.”—James H. Stock, John F. Kennedy School of Government, Harvard University
