000			02099nam a2200169Ia 4500
020			_a8131509609
082			_a658.4033 WOO
100			_aWooldridge, Jeffrey M
245			_a Econometrics
260			_b Cengage _aNew Delhi _c2009
300			_a631p. _b24cm ; Pbk
500			_aGratis
505			_a1. 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 Asymptotic 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. Index.
520			_aEconometrics illustrates how empirical researchers think about and apply econometric methods in real-world practice. 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.
650			_aEconometrics _aRegression analysis _aEconomics - Inference
942			_cBK
999			_c98597 _d98597