A Course in Econometrics

A Course in Econometrics

A Course in Econometrics

A Course in Econometrics

Excerpt

The primary objective of this book is to prepare students for empirical research. But it also serves those who will go on to advanced study in econometric theory. Recognizing that readers will have diverse backgrounds and interests, I appeal to intuition as well as to rigor, and draw on a general acquaintance with empirical economics. I encourage readers to develop a critical sense: students ought to evaluate, rather than simply accept, what they read in journals and textbooks.

The book derives from lecture notes that I have used in the first-year graduate econometrics course at the University of Wisconsin. Students enroll from a variety of departments, including agricultural economics, finance, accounting, industrial relations, and sociology, as well as economics. All have had a year of calculus, a semester of linear algebra, and a semester of statistical inference. Some have had much more course work, including probability theory, mathematical statistics, and econometrics. Others have had substantial empirical research experience.

All of the material can be covered--indeed has been covered--in two semesters. To make that possible, I focus on a few underlying principles, rather than cataloging many potential methods. To accommodate students with varied preparation, the book begins with a review of elementary statistical concepts and methods, before proceeding to the regression model and its variants.

Although the models covered are quite standard, the approach taken is somewhat distinctive. The conditional expectation function (CEF) is introduced as the key feature of a multivariate population for economists who are interested in relations among economic variables. The CEF describes how the average value of one variable varies with values of the other variables in the population--a very simple concept. Another key feature of a multivariate population is the linear projection, or best . . .

A Course in Econometrics is likely to be the text most thoroughly attuned to the needs of your students. Derived from the course taught by Arthur S. Goldberger at the University of Wisconsin-Madison and at Stanford University, it is specifically designed for use over two semesters, offers students the most thorough grounding in introductory statistical inference, and offers a substantial amount of interpretive material. The text brims with insights, strikes a balance between rigor and intuition, and provokes students to form their own critical opinions. A Course in Econometrics thoroughly covers the fundamentals—classical regression and simultaneous equations—and offers clear and logical explorations of asymptotic theory and nonlinear regression. To accommodate students with various levels of preparation, the text opens with a thorough review of statistical concepts and methods, then proceeds to the regression model and its variants. Bold subheadings introduce and highlight key concepts throughout each chapter. Each chapter concludes with a set of exercises specifically designed to reinforce and extend the material covered. Many of the exercises include real micro-data analyses, and all are ideally suited to use as homework and test questions.
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