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, Global Edition, presents this ever-growing area at an accessible graduate level. The book first introduces students to basic techniques, a rich variety of models, and underlying theory that is easy to put into practice. It then presents students with a sufficient theoretical background to understand advanced techniques and to recognise new variants of established models. This focus, along with hundreds of worked numerical examples, ensures that students can apply the theory to real-world application and are prepared to be successful economists in the field The full text downloaded to your computer

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  Inhalt

• 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