Economics

For first year graduate courses in econometrics for social scientists.

Greene, 6e 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.

For first year graduate courses in econometrics for social scientists.

 

Greene, 6e 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.

 

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?

 

The sixth edition of this text has two objectives, intended to bridge the gap between the field of econometrics and the professional literature for graduate students in social sciences:

 

1)      To introduce students to applied econometrics

2)      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.

 

 

What are some important concepts you feel are necessary in understanding the fundamental concepts of econometrics?

 

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:

o       Appendix A; Pg. 945

o       Appendix E; Pg. 1061

 

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; Pg. 400

o       Generalized method of moments estimator; Pg. 441

o       Maximum likelihood estimation; Pg. 482

o       Bayesian methods; Pg. 600

 

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; Pg. 523

o       Random effects model; Pg. 200

o       Regressions model; Pg. 603

 

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.

 

<>What are some important concepts you feel are necessary in understanding the fundamental concepts of econometrics?

 

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:

o       Appendix A

o       Appendix E

 

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

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.

 

 OTHER TOPICS OF DISTINCTION

 

*New full chapter on simulation based estimation
*New chapter on counts and duration models.
*200+ new pages on panel data
*Replaced many applications with newer applications from the literature
*Replaced or reworked many of the examples
*Some new exercises, including in almost every chapter new project
length suggested applications.
*New material at several specific points for newer applications and
methods in the literature.

Preface
Chapter 1 – Introduction
Chapter 2 – The Classical Multiple Linear Regression Model
Chapter 3 – Least Squares
Chapter 4 – Statistical Properties of the Least Squares Estimator
Chapter 5 – Inference and Prediction
Chapter 6 – Functional Form and Structural Change
Chapter 7 – Specification Analysis and Model Selection
Chapter 8 – Generalized Regression Model and Heteroscedasticity
Chapter 9 – Models for Panel Data
Chapter 10 –Systems of Regression Equations
Chapter 11 – Nonlinear Regression Models
Chapter 12 – Instrumental Variables Estimation
Chapter 13 – Simultaneous-Equations Model
Chapter 14 – Estimation Frameworks in Econometrics
Chapter 15 – Minimum Distance Estimation and the Generalized Method of Moments
Chapter 16 – Maximum Likelihood Estimation
Chapter 17 – Simulation Based Estimation and Inference
Chapter 18 – Bayesian Estimation and Inference
Chapter 19 – Serial Correlation
Chapter 20 – Models With Lagged Variables
Chapter 21 – Time-Series Models
Chapter 22 – Nonstationary Data
Chapter 23 – Models for Discrete Choice
Chapter 24 – Truncation, Censoring and Sample Selection
Chapter 25 – Models for Event Counts and Duration
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
References
Author Index
Subject Index

Graduate Econometrics  [CORE TEXTS]   (Economics)

Review Copy, 6/E
Greene
©2008 | Prentice Hall | Cloth; 1216 pp | Instock
ISBN-10: 0136003834 | ISBN-13: 9780136003830

Student Solutions Manual, 6/E
Greene
©2008 | Prentice Hall | On-line Supplement; 160 pp | Instock
ISBN-10: 0135132444 | ISBN-13: 9780135132449
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