Introductory Econometrics: A Modern Approach / Edition 4by Jeffrey M. Wooldridge
Pub. Date: 03/27/2008
Publisher: Cengage Learning
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… See more details below
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.
- Cengage Learning
- Publication date:
- Edition description:
- with Economic Applications, Data Sets, Student Solutions Manual Printed Access Card
- Product dimensions:
- 7.50(w) x 9.50(h) x 1.60(d)
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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