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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:
The arrangement of this text begins with formal presentation of the development of the fundamental pillar of econometrics. Some highlights include:
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.
Econometric Analysis, 7e by Greene is a major revision both in terms of organization of the material and in terms of new ideas and treatments.
In the seventh edition, Greene substantially rearranged the early part of the book to produce a more natural sequence of topics for the graduate econometrics course. For example,
New results on prediction
Greater and earlier emphasis on instrumental variables and endogeneity
Additional results on basic panel data models
New applications and examples, with greater detail
Greater emphasis on specific areas of application in the advanced material
New material on simulation based methods, especially bootstrapping and Monte Carlo studies
Several examples that explain interaction effects
Specific recent applications including quantile regression
New applications in discrete choice modeling
New material on endogeneity and its implications for model structure
Topics that have been expanded or given greater emphasis include:
Treatment effects, bootstrapping, simulation based estimation, robust estimation, missing and faulty data, and a variety of different new methods of discrete choice analysis in micro econometrics.
Added or expanded material on techniques recently of interest, such as quintile regression and stochastic frontier models.
Highlights of the revision - in general terms,
Increased the focus on robust methods.
Greene placed discussions of specification tests at several points, consistent with the trend in the literature to examine more closely the fragility of heavily parametric models.
A few of the specific new applications are as follows:
Simulation based estimation has been considerably expanded in chapter 15. It now includes substantially more material on bootstrapping standard errors and confidence intervals. The Krinsky and Robb (1986) approach to asymptotic inference has been placed here as well.
A great deal of attention has been focused in recent papers on how to understand interaction effects in nonlinear models. Chapter 7 contains a lengthy application of interaction effects in a nonlinear (exponential) regression model. The issue is revisited in Chapter 17.
As an exercise that will challenge the student’s facility with asymptotic distribution theory, Greene added a detailed proof of the Murphy and Topel (2002) result for two step estimation in Chapter 14.
Sources and treatment of endogeneity appear at various points, for example an application of inverse probability weighting to deal with attrition in Chapter 17.
OTHER TOPICS OF DISTINCTION
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 Variables—Truncation, 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
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