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The modern approach of this text recognizes that econometrics has moved from a specialized mathematical description of economics to an applied interpretation based on empirical research techniques. It bridges the gap between the mechanics of econometrics and modern applications of econometrics by employing a systematic approach motivated by the major problems facing applied researchers today. Throughout the text, the emphasis on examples gives a concrete reality to economic relationships and allows treatment of interesting policy questions in a realistic and accessible framework.
Jeffrey M. Wooldridge is a University Distinguished Professor of Economics at Michigan State University, where he has taught since 1991. From 1986 to 1991, he served as 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 work has earned numerous awards, including the 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. He has also served on the editorial boards of the Journal of Econometrics and the Review of Economics and Statistics. Dr. Wooldridge received his B.A. with majors in computer science and economics from the University of California, Berkeley, and received his Ph.D. in economics from the University of California, San Diego.
1. The Nature of Econometrics and Economic Data. PART I. REGRESSION ANALYSIS WITH CROSS SECTION 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 II. 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 III. ADVANCED TOPICS. 13. Polling Cross Sections Across Time: Simple Panel Data Methods. 14. Advanced Panel Data Methods. 15. Instrumental Variables Estimation and Two State Least Squares. 16. Introduction to Simultaneous Equations Models. 17. Limited Dependent Variable Models and Sample Selection Corrections. 18. Advanced Time Series Topics. 19. Carrying Out an Empirical Project.