A Course in Econometrics

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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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Contents

Relations
1
Univariate Probability Distributions
11
Multicollinearity
23
Univariate Case
26
Heteroskedasticity and Autocorrelation
28
Bivariate Probability Distributions
34
Bivariate Case
44
7
67
Classical Normal Regression
204
Hypothesis Testing
214
Inference with o Unknown
223
Issues in Hypothesis Testing
233
Regression Strategies
254
Regression with X Random
264
Time Series
274
Generalized Classical Regression
292

8
79
Asymptotic Distribution Theory
94
Bivariate Case
106
Advanced Estimation Theory
128
Estimating a Population Relation
138
Multiple Regression
150
Classical Regression
160
Exercises
168
Regression Algebra
182
Multivariate Normal Distribution
195
Nonlinear Regression
308
Regression Systems
323
Squares
329
Structural Equation Models
337
SimultaneousEquation Model
351
Estimation in the SimultaneousEquation Model
365
Appendix A Statistical and Data Tables
381
Appendix B Getting Started in GAUSS
391
References
397
Copyright

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About the author (1991)

Arthur S. Goldberger was Professor of Economics, Emeritus, at the University of Wisconsin-Madison.

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