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(More customer reviews)This textbook for advanced undergraduates or first-year graduate students leads the reader through the basic concepts and statistical procedures of econometrics. The extensive use of matrix notation allows the reader to easily apply Goldberger's formulae to actual data (especially with computer packages such as Gauss and Matlab). Although the author gives brief treatments which sometimes require several readings to be fully understood, he is by no means unclear or confusing. Quite the contrary, as the book is very concise. Sections which I found especially useful and interesting were: 22.5 Statistical versus Economic Significance, 23 Multicollinearity (actually humorous!), and 24 Regression Strategies.
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This text prepares first-year graduate students and advanced undergraduates for empirical research in economics, and also equips them for specialization in econometric theory, business, and sociology.
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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