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  • Edition: 8th
  • Format: Hardcover
  • Copyright: 2017-03-30
  • Publisher: Pearson

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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

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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