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Description
For a one-year graduate course in Econometrics.
This text has two objectives. The first is to introduce students to applied econometrics, including basic techniques in regression analysis and some of the rich variety of models that are used when the linear model proves inadequate or inappropriate. The second is to present students with sufficient theoretical background that they will recognize new variants of the models learned about here as merely natural extensions that fit within a common body of principles. The Fifth Edition features a complete update of techniques and developments, a reorganization of material for improved presentation, and new material and applications.

Features
NEW - Major reorganization of material.
Presents the material in a more natural and orderly way.
NEW - Complete coverage of new topics—Especially time series and panel data.
Keeps students informed of the latest and most interesting developments in the field.
NEW - More numerical examples and applications—Detailed worked applications.
Shows students how to do the computations.
NEW - A new balance between theory and application.
Gives advanced students the challenge they need, while providing new, extended examples for more applied learning.
EA/LimDep exclusive software package updated for this edition—Customized version of commercial computer program used by governments and industry. Can be used for nearly all computations described in book. Available to download from prenhall.com/greene along with data sets and additional exercises and solutions.
Complete coverage of methods—Includes discussion of Classical, Bayesian, GMM, Maximum likelihood, time series, cross section, and panels.
Gives students a balanced coverage consistently throughout the text.
A focus on both linear and nonlinear techniques.
Makes nonlinear estimation easy.
Seven full chapters on the linear multiple regression model and extensive integration throughout the text.
Gives students a thorough background in the fundamental building blocks of econometrics.
Applied orientation.
Teaches students how to do econometrics, not just work through proofs of asymptotic properties.
Theoretical material—e.g., extensive explanations of the mechanics of GMM estimation, nonlinear least squares, maximum likelihood estimation (GARCH models), asymptotic results for regression models, etc.
Prepares students to recognize new variants of the models they learn as merely natural extensions that fit within a common body of principles.
Consistent mathematical level and notation throughout with self-contained summaries of matrix algebra, statistical theory, and mathematical statistics.
Provides students with early review/instruction of the mathematics they will encounter in the main part of the text.
Surveys a wide range of topics in econometrics.
Helps students appreciate the important common foundation of all of the fields and to use the tools that they employ to move from the basics to more advanced study—e.g., on limited dependent variables, duration models, and time series.

New To This Edition
Major reorganization of material.
Presents the material in a more natural and orderly way.
Complete coverage of new topics—Especially time series and panel data.
Keeps students informed of the latest and most interesting developments in the field.
More numerical examples and applications—Detailed worked applications.
Shows students how to do the computations.
A new balance between theory and application.
Gives advanced students the challenge they need, while providing new, extended examples for more applied learning.

Table of Contents
1. Introduction.
2. The Classical Multiple Linear Regression Model.
3. Least Squares.
4. Finite Sample Properties of the Least Squares Estimator.
5. Large Sample Properties of the Least Squares and Instrumental Variables Estimators.
6. Inference and Prediction.
7. Functional Form and Structural Change.
8. Specification Analysis and Model Selection.
9. Nonlinear Regression Models.
10. Nonspherical Disturbances—The Generalized Regression Model.
11. Heteroscedasticity.
12. Serial Correlation.
13. Models for Panel Data.
14. Systems of Regression Equations.
15. Simultaneous-Equations Models.
16. Estimation Frameworks in Econometrics.
17. Maximum Likelihood Estimation.
18. The Generalized Method of Moments.
19. Models with Lagged Variables.
20. Time-Series Models.
21. Models for Discrete Choice.
22. Limited Dependent Variable and Duration Models.
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.