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Econometric Analysis, Global Edition

Paperback Engels 2019 9781292231136
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This title is a Pearson Global Edition. The Editorial team at Pearson has worked closely with educators around the world to include content which is especially relevant to students outside the United States.
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 recognize 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.




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PART I. The Linear Regression Model
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

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