A standard text/reference in courses that include basic techniques in regression analysis and extensions used when linear models prove inadequate or inappropriate. Areas of application include Economics, Sociology, Political Science, Medical Research, Transport Research, and Environmental Economics. This book introduces students to the broad field of applied econometrics. An effective bridge to both on-the-job problems and to the professional literature, it features extensive applications and presents sufficient theoretical background to enable students to recognize new variants of the models that they learn about here as merely natural extensions that fit within a common body of principles.

• NEW - New Applications—Most of the material on the consumption function has been removed from the book, new applications of ARDL models have been provided, and applications of Nonlinear Regression have been reworked and somewhat simplified.

  • Applications are fresh and apply to real world use. Ex.___

• NEW - Major revision of chapters on applied econometrics (Chs. 17-20)—e.g., a thorough overhaul of the chapters on Time Series Models, and new sections on specification and estimation of autoregressive distributed lag models, methodological issues in dynamic models, the frequency domain and spectral analysis, robust covariance matrix estimation, and goodness of fit in binary choice models.

  • Increased emphasis on nonlinear models brings the textbook in line with current practice. Ex.___

• NEW - Free econometric software—Included in every text, a customized student version of LIMDEP Software, plus datasets and worked examples.

  • Allows students to work with realistic data sets and challenging empirical analyses as a routine part of their econometrics training. (See CD in back of text.)

• NEW - Chs. 17 and 18 have been thoroughly revised—To reflect current trends in time series analysis, including frequency domain and long term memory models.

  • Provides the most current trends in econometrics. Ex.___

• NEW - Reorganized coverage of the Classical Multiplier Regression Model.

  • Provides students with a more accessible discussion of this difficult topic. Ex.___

• Consistent mathematical level and notation throughoutwith self-contained summaries of matrix algebra, statistical theory, and mathematical statistics.

  • Provides students with early review/instruction (Chs. 2-4) of the mathematics they will encounter in the main part of the text. Ex.___

• Extensive applications—e.g., for Models for Panel Data, all applications are replaced with a more interesting data set and new results, and several applications are expanded with explanations of computations added; for Inference and Prediction, better applications of testing for structural change are provided; and for The Classical Multiple Linear Regression Model, an important new application, “Oaxaca Decomposition”, is introduced.

  • Makes theory come to life and prepares students for problems they will likely encounter on the job. Ex.___

• 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. Prepares them to move comfortably from the basics to more advanced study in one or more of the specialized areas. Ex.___

• Three full chapters on the linear multiple regression model and extensive integration throughout the text.

  • Gives students a thorough ground in the fundamental building block of econometrics. Ex.___

• Current topics in applied econometrics.

  • Explores GMM estimation methods, Lagrange multiplier tests, time series analysis, and the analysis of qualitative and limited dependent variable models. Ex.___

• 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. Ex.___

• A bridge to the professional literature.

  • Exposes students to a fair amount of material that will extend beyond many first courses in econometrics—e.g., on limited dependent variables, duration models, and time series. Ex.___

• New Applications—Most of the material on the consumption function has been removed from the book, new applications of ARDL models have been provided, and applications of Nonlinear Regression have been reworked and somewhat simplified.

  • Applications are fresh and apply to real world use. Ex.___

• Major revision of chapters on applied econometrics (Chs. 17-20)—e.g., a thorough overhaul of the chapters on Time Series Models, and new sections on specification and estimation of autoregressive distributed lag models, methodological issues in dynamic models, the frequency domain and spectral analysis, robust covariance matrix estimation, and goodness of fit in binary choice models.

  • Increased emphasis on nonlinear models brings the textbook in line with current practice. Ex.___

• Free econometric software—Included in every text, a customized student version of LIMDEP Software, plus datasets and worked examples.

  • Allows students to work with realistic data sets and challenging empirical analyses as a routine part of their econometrics training. (See CD in back of text.)

• Chs. 17 and 18 have been thoroughly revised—To reflect current trends in time series analysis, including frequency domain and long term memory models.

  • Provides the most current trends in econometrics. Ex.___

• Reorganized coverage of the Classical Multiplier Regression Model.

  • Provides students with a more accessible discussion of this difficult topic. Ex.___

 1. Introduction.


 2. Matrix Algebra.


 3. Probability and Distribution Theory.


 4. Statistical Inference.


 5. Computation and Optimization.


 6. The Classical Multiple Linear Regression Model: Specification and Estimation.


 7. Inference and Prediction.


 8. Functional Form, Nonlinearity, and Specification.


 9. Large-Sample Results and Alternative Estimators for the Classical Regression Model.


10. Nonlinear Regression Models.


11. Nonspherical Disturbances, Generalized Regression, and GMM Estimation.


12. Heteroscedasticity.


13. Autocorrelated Disturbances.


14. Models for Panel Data.


15. Systems of Regression Equations.


16. Simultaneous Equations Models.


17. Regressions with Lagged Variables.


18. Time-Series Models.


19. Models with Discrete Dependent Variables.


20. Limited Dependent Variable and Duration Models.


Appendix A. Data Sets Used in Applications.


Appendix B. Statistical Tables.


References.


Author Index.


Subject Index.

CD-ROM, Limdep Software, 4/E
Greene
©2000  |  Prentice Hall  |  CD-ROM Only  |  Not Yet Published
ISBN-10: 0130141313  |  ISBN-13: 9780130141316
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