Introductory Econometrics: A Modern Approach » (4th Edition)

Jeffrey M. Wooldridge is University Distinguished Professor of Economics at Michigan State University, where he has taught since 1991. From 1986 to 1991, he was Assistant Professor of Economics at the Massachusetts Institute of Technology. Dr. Wooldridge has published more than three dozen articles in internationally recognized journals, as well as several book chapters. He is also the author of Econometric Analysis of Cross Section and Panel Data. His awards include an Alfred P. Sloan Research Fellowship, the Multa Scripsit award from ECONOMETRIC THEORY, the Sir Richard Stone prize from the JOURNAL OF APPLIED ECONOMETRICS, and three graduate teacher-of-the-year awards from MIT. A fellow of the ECONOMETRIC SOCIETY and of the JOURNAL OF ECONOMETRICS, Dr. Wooldridge has been editor of the JOURNAL OF BUSINESS AND ECONOMIC STATISTICS and econometrics co-editor of ECONOMICS LETTERS, and he has served on the editorial boards of the JOURNAL OF ECONOMETRICS and the REVIEW OF ECONOMICS AND STATISTICS. Dr. Wooldridge received his bachelor of arts, with majors in computer science and economics, from the University of California, Berkeley, in 1982, and received his doctorate in economics in 1986 from the University of California, San Diego.

Adaptable to both advanced undergraduate and graduate courses, this textbook develops statistical methods for estimating economic relationships and evaluating government policy, covering multiple regression analysis with cross-sectional data, econometric analysis of time series data, panel data methods, and endogenous explanatory variables. The third edition reorders the assumptions for simple and multiple regression and adds more data sets. Annotation ©2005 Book News, Inc., Portland, OR

1. The Nature of Econometrics and Economic Data. PART 1: REGRESSION ANALYSIS WITH CROSS-SECTIONAL DATA. 2. The Simple Regression Model. 3. Multiple Regression Analysis: Estimation. 4. Multiple Regression Analysis: Inference. 5. Multiple Regression Analysis: OLS Asymptotics. 6. Multiple Regression Analysis: Further Issues. 7. Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables. 8. Heteroskedasticity. 9. More on Specification and Data Problems. PART 2: REGRESSION ANALYSIS WITH TIME SERIES DATA. 10. Basic Regression Analysis with Time Series Data. 11. Further Issues in Using OLS with Time Series Data. 12. Serial Correlation and Heteroskedasticity in Time Series Regressions. PART 3: ADVANCED TOPICS. 13. Pooling Cross Sections across Time: Simple Panel Data Methods. 14. Advanced Panel Data Methods. 15. Instrumental Variables Estimation and Two Stage Least Squares. 16. Simultaneous Equations Models. 17. Limited Dependent Variable Models and Sample Selection Corrections. 18. Advanced Time Series Topics. 19. Carrying out an Empirical Project. APPENDICES. Appendix A Basic Mathematical Tools. Appendix B Fundamentals of Probability. Appendix C Fundamentals of Mathematical Statistics. Appendix D Summary of Matrix Algebra. Appendix E The Linear Regression Model in Matrix Form. Appendix F Answers to Chapter Questions. Appendix G Statistical Tables. References. Glossary. Index.