Econometric Analysis: International Edition7th Edition
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For first-year graduate courses in Econometrics for Social Scientists.
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.
This text serves as a bridge between an introduction to the field of econometrics and the professional literature for graduate students in the social sciences, focusing on applied econometrics and theoretical concepts.
Table of Contents
Part I: The Linear Regression Model
Chapter 1: Econometrics
Chapter 2: The Linear Regression Model
Chapter 3: Least Squares
Chapter 4: The Least Squares Estimator
Chapter 5: Hypothesis Tests and Model Selection
Chapter 6: Functional Form and Structural Change
Chapter 7: Nonlinear, Semiparametric, and Nonparametric Regression Models
Chapter 8: Endogeneity and Instrumental Variable Estimation
Part II: Generalized Regression Model and Equation Systems
Chapter 9: The Generalized Regression Model and Heteroscedasticity
Chapter 10: Systems of Equations
Chapter 11: Models for Panel Data
Part III: Estimation Methodology
Chapter 12: Estimation Frameworks in Econometrics
Chapter 13: Minimum Distance Estimation and the Generalized Method of Moments
Chapter 14: Maximum Likelihood Estimation
Chapter 15: Simulation-Based Estimation and Inference
Chapter 16: Bayesian Estimation and Inference
Part IV: Cross Sections, Panel Data, and Microeconometrics
Chapter 17: Discrete Choice
Chapter 18: Discrete Choices and Event Counts
Chapter 19: Limited Dependent VariablesTruncation, Censoring, and Sample Selection
Part V: Time Series and Macroeconometrics
Chapter 20: Serial Correlation
Chapter 21: Models with Lagged Variables
Chapter 22: Time-Series Models
Chapter 23: 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
Appendix G: Statistical Tables
How much theoretical background on the study of econometrics do your students have before entering your classroom? By the end of the semester, do they typically walk away with a solid understanding of both 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 recognize 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 1-7
- The generalized regression model and non-linear regressions; Chapters 8-11
- Instrumental variables and its application to the estimation of simultaneous equations models; Chapters 12 and 13
- Matrix Algebra — This text makes heavy use of this feature. All the matrix algebra needed in the text contains a description of numerical methods that will be useful to practicing econometricians. This can be found in:
What types of real-world examples do your students find most engaging? How does this help them 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. Chapters 14-18 present different estimation methodologies such as:
o Parametric and nonparametric methods
o Generalized method of moments estimator
o Maximum likelihood estimation
o Bayesian methods
Do you tend to provide students with a broad coverage of all possible alternatives to the maximum likelihood estimator (MLE) or would you rather focus in on what is most used by researchers in the real-world?
Where there exist robust alternatives to the MLE, such as moments based estimators for the random effects linear model, researchers have tended to gravitate to them. Our treatment of maximum likelihood estimation is more compartmentalized in this edition. For example, Chapter 16 has been streamlined into one presentation of the ML estimator, covering the:
o Multiplicative heteroscedasticity model
o Random effects model
o Regressions model
OTHER POINTS OF DISTINCTION
How often do you incorporate information from outside sources into the classroom? Do you ever share articles and journals to your class 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.
Is it ever difficult to formulate a concrete outline with some econometrics books on the market?
• A substantial rearrangement of the material has been made, by using advice of readers to make it easier to construct a course outline with this text.