econometric analysis 6 book cased - 6ª edição
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 background.
Features
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 background.
What are some important concepts you feel are necessary in understanding the foundations of econometrics?
Some of the crucial elements found in Greenes text includes:
Matrix Algebra - This text makes heavy use of this feature. With matrices, the unity of a variety of results will emerge without being obscured by a curtain of summation signs. 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. XX)
o Appendix E, (Pg. XX)
Development of the Fundamental Pillar of Econometrics - The arrangement of this text begins with the classical linear multiple regression model (Chapter 1-7), followed by the generalized regression model and non-linear regressions (Chapter 8-11), instrumental variables (Chapter 12-13), estimation methods and generalized method movements (Chapter 14-15), and Maximum likelihood estimation (Chapter 16). The final chapters include such topics as, Monte Carlo Analysis, Bayesian Methods, serial correlation, truncation, and analysis of events.
Do you tend to provide 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:
The multiplicative heteroscedasticity model, the random effects model, the seemingly unrelated regressions model, and a few others have been moved to a single presentation of the ML estimator in Chapter 16, where they are developed as applications (Pg. XX).
Later in the book, in the section on microeconometrics, the MLE reemerges as the leading estimator (Pg. XX).
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?
There are new and interesting developments in the field, particularly in the areas of microeconometrics (panel data and models for discrete choice) and, of course, in time series which continues its rapid development, that students will enjoy learning about that I have included in this text (Pg. XX).
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.
New to this Edition
Do you tend to provide 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:
The multiplicative heteroscedasticity model, the random effects model, the seemingly unrelated regressions model, and a few others have been moved to a single presentation of the ML estimator in Chapter 16, where they are developed as applications (Pg. XX).
Later in the book, in the section on microeconometrics, the MLE reemerges as the leading estimator (Pg. XX).
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?
There are new and interesting developments in the field, particularly in the areas of microeconometrics (panel data and models for discrete choice) and, of course, in time series which continues its rapid development, that students will enjoy learning about that I have included in this text (Pg. XX).
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.
| Prentice Hall - Importados (Grupo Pearson) |
| 0135132452 |
| 9780135132456 |
| Inglês |
| Importado |
| 6 |
|
| 6/9/2007 |
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