For first-year graduate courses in Econometrics for Social Scientists. This title is a Pearson Global Edition. The Editorial team at Pearson has worked closely with educators around the world to include content which is especially relevant to students outside the United States. This text 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.
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Table of Contents 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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"The book can be considered a sort of bible for all who need both a soft but rigorous introduction to econometrics as well as advanced econometric treatments." Dr Houdou Basse Mama University of Hamburg, Germany
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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: To introduce students to applied econometrics 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:  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 cons
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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,    Reorganization of the early material that is taught in the first semester course, including: All material on hypothesis testing and specification presented in a single chapter 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   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.
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Produktdetaljer

ISBN
9780273753568
Publisert
2011
Utgave
7. utgave
Utgiver
Vendor
Pearson Education Limited
Vekt
1922 gr
Høyde
230 mm
Bredde
190 mm
Dybde
50 mm
Aldersnivå
U, 05
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
1240

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