Introductory Econometrics: A Modern Approach / Edition 4

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Overview

INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 4e illustrates how empirical researchers think about and apply econometric methods in real-world practice. The text's unique approach reflects the fact that undergraduate econometrics has moved beyond just a set of abstract tools to being genuinely useful for answering questions in business, policy evaluation, and forecasting environments. The systematic approach, which reduces clutter by introducing assumptions only as they are needed, makes absorbing the material easier and leads to better econometric practices. Its unique organization separates topics by the kinds of data being analyzed, leading to an appreciation for the important issues that arise in drawing conclusions from the different kinds of data economists use. Packed with relevant applications, INTRODUCTORY ECONOMETRICS offers a wealth of interesting data sets that can be used to reproduce the examples in the text or as the starting point for original research projects.

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Editorial Reviews

From the Publisher
"The most important strength of this text is its close connection (in terms of both style/structure and content) to the way empirical research is actually conducted. In particular, the emphasis on whether or not an estimated relationship can be considered causal is central to econometric practice today, and this book, unlike many others, makes that clear. The book is very comprehensive, with a wide range of topics that other introductory econometrics books do not always include but that are very common in research (e.g. panel data)."

"I use Wooldridge because it provides a nice balance of equations, examples, and well-written intuitive descriptions of econometrics. The text provides clarity, discussion, and analytic rigor, with chapter problems that are both paper and computer-based. The review of chapters in the Instructor's Guide is useful, especially as a guide in deciding how to pare down the topics to a reasonable size for one semester."

"One of the biggest strengths of this Wooldridge text is the way in which it brings together the majority of mainstream econometric models in an easy to understand and coherent manner that is suitable to a wide audience from undergraduates, graduates, and a reference text for applied economists."

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Product Details

  • ISBN-13: 9780324581621
  • Publisher: Cengage Learning
  • Publication date: 3/27/2008
  • Edition description: with Economic Applications, Data Sets, Student Solutions Manual Printed Access Card
  • Edition number: 4
  • Pages: 896
  • Sales rank: 1,005,153
  • Product dimensions: 7.50 (w) x 9.50 (h) x 1.60 (d)

Meet the Author

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.

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Table of Contents

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.

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