Econometric Analysis, Global Edition

For first-year graduate courses in Econometrics for Social Scientists.

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. Features

• Presents students with a solid understanding of applied econometrics and theoretical concepts
• This text has two objectives that are intended to help students bridge the gap between the field of econometrics and the professional literature for graduate students in social sciences:
  • To introduce students to applied econometrics.
  • To present students with sufficient theoretical background so they will recognise new variants of the models learned about here as natural extensions of common principles.
• The arrangement of this text begins with formal presentation of the development of the fundamental pillar of econometrics. Some highlights include:
  • The classical linear regression model (Chapters 1­ to 7).
  • The generalised regression model and non­linear regressions (Chapters 8­ to 11).
  • Instrumental variables and their application to the estimation of simultaneous equations models (Chapters 12 and 13).
  • A Matrix Algebra contains descriptions of numerical methods that are useful to practicing econometricians.
• Provides engaging real-­world examples to help students understand course material
• Once the fundamental concepts are addressed, the second half proceeds to explain the involved methods of analysis that contemporary researchers use in analysis of real-world data.
• Offers details on the maximum likelihood estimator (MLE) and broad coverage of all possible alternatives to MLE
• Shares articles and journals featuring the most recent developments in econometrics

• New and interesting developments have been included in the area of microeconometrics (panel data and models for discrete choice) and in time series which continues its rapid development.
• A revised presentation throughout the book streamlines the development of topics and improves clarity.
• Material related to Causal Inference now features earlier in the book to better align with students' studies.
• Chapter 6 features discussion of difference regression as a method and regression discontinuity designs.
• Chapter 8 now includes the analysis of treatment effects as well as more detailed treatment of instrumental variable methods.
• Chapter 10 shifts further from its prior focus on formal simultaneous linear equations models to systems of regression equations.
• Chapters 4 (Least Squares), 6 (Functional Forms), 8 (Endogeneity), 10 (Equation Systems) and 11 (Panel Data) have been heavily revised to emphasise both contemporary econometric methods and their applications.
• Additional examples and extracts from applications feature in the 8th Edition. Applications are drawn from a range of different fields including industrial organisation, transportation, health economics, popular culture and sports, urban development, and labor economics.
• Robust estimation and inference methods are now more extensively woven into the general methodology and practice in the text.

• 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