Econometric Analysis, 8th edition

  • William H. Greene


For first-͹ear 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

    Published by Pearson (March 30th 2017) - Copyright © 2018