# A Course in Econometrics

Harvard University Press, 1991 - Business & Economics - 405 pages

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 microdata analyses, and all are ideally suited to use as homework and test questions.

### Contents

 Relations 1 Univariate Probability Distributions 11 Regression Algebra 17 Exercises 32 Exercises 41 Exercises 54 Normal Distributions 68 Univariate Case 80
 Issues in Hypothesis Testing 233 Exercises 243 Multicollinearity 245 Regression Strategies 254 Regression with X Random 264 Exercises 273 Generalized Classical Regression 292 Exercises 299

 Asymptotic Distribution Theory 94 Exercises 104 Advanced Estimation Theory 128 Estimating a Population Relation 138 Classical Regression 144 Exercises 158 Exercises 168 Classical Normal Regression 189 Exercises 202 Exercises 213 Exercises 220
 Heteroskedasticity and Autocorrelation 300 Nonlinear Regression 308 Regression Systems 323 Structural Equation Models 337 SimultaneousEquation Model 349 Exercises 363 Estimation in the SimultaneousEquation Model 365 Appendix A Statistical and Data Tables 381 Appendix B Getting Started in GAUSS 391 References 397 Copyright