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For first-¿year graduate courses in Econometrics for Social Scientists.
Bridging the gap between social science studies and econometric analysis
Designed to bridge the gap between social science studies and field-econometrics, Econometric Analysis, 8th Edition presents this ever-growing area at an accessible level. The book first introduces readers to basic techniques, a rich variety of models, and underlying theory that is easy to put into practice. It then presents readers with a sufficient theoretical background to understand advanced techniques and to recognize new variants of established models. This focus, along with hundreds of worked numerical examples, ensures that readers can apply the theory to real-world application and are prepared to be successful economists in the field.
Table of Contents:
PART I. The Linear Regression Model
1.Econometrics
2. The Linear Regression Model
3. Least Squares
4. Estimating the Regression Model by Least Squares
5. Hypothesis Tests and Model Selection
6. Functional Form, Difference in Differences and Structural Change
7. Nonlinear, Semiparametric and Nonparametric Regression Models
8. Endogeneity and Instrumental Variable Estimation
PART II. Generalized Regression Model and Systems of Equations
9. The Generalized Regression Model and Heteroscedasticity
10. Systems of Regression Equations
11. Models for Panel Data
PART III. Estimation Methodology
12. Estimation Frameworks in Econometrics
13. Minimum Distance Estimation and the Generalized Method of Moments
14. Maximum Likelihood Estimation
15. Simulation-Based Estimation and Inference and Random Parameter Models
16. Bayesian Estimation and Inference
PART IV. Cross Sections, Panel Data and Microeconometrics
17. Binary Outcomes and Discrete Choices
18. Multinomial Choices and Event Counts
19. Limited Dependent Variables, Truncation, Censoring and Sample Selection
PART V. Time Series and Macroeconometrics
20. Serial Correlation
21. Nonstationary Data
PART VI. Appendices
Appendix A: Matrix Algebra
Appendix B: Probability and Distribution Theory
Appendix C: Estimation and Inference
Appendix D: Large Sample Distribution Theory
Appendix E: Computation and Optimization
Appendix F: Data Sets Used In Applications
