Edward W. Frees

Longitudinal and Panel Data

Analysis and Applications in the Social Sciences

• Gebundenes Buch

Produktbeschreibung

Short description/annotation
An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.

Main description
This focuses on models and data that arise from repeated observations of a cross-section of individuals, households or companies. These models have found important applications within business, economics, education, political science and other social science disciplines. The author introduces the foundations of longitudinal and panel data analysis at a level suitable for quantitatively oriented graduate social science students as well as individual researchers. He emphasizes mathematical and statistical fundamentals but also describes substantive applications from across the social sciences, showing the breadth and scope that these models enjoy. The applications are enhanced by real-world data sets and software programs in SAS and Stata.

Table of contents:
1. Introduction; Part I. Linear Models: 2. Fixed effects models; 3. Models with random effects; 4. Prediction and Bayesian Inference; 5. Multilevel models; 6. Random regressors; 7. Modeling issues; 8. Dynamic models; Part II. Nonlinear Models: 9. Binary dependent variables; 10. Generalized linear models; 11. Categorical dependent variables and survival models; Appendix A. Elements of Matrix Algebra; Appendix B. Normal distribution; Appendix C. Likelihood-based inference; Appendix D. Kalman Filter; Appendix E. Symbols and notation; Appendix F. Selected longitudinal and panel data sets; Appendix G. References.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 484
• Erscheinungstermin: 1. April 2010
• Englisch
• Abmessung: 235mm x 157mm x 33mm
• Gewicht: 930g
• ISBN-13: 9780521828284
• ISBN-10: 0521828287
• Artikelnr.: 21400655

Autorenporträt

E. W. Frees is a Professor of Business at the University of Wisconsin-Madison and is holder of the Fortis Health Insurance Professorship of Actuarial Science. He is a Fellow of both the Society of Actuaries and the American Statistical Association. He has served in several editorial capacities including Editor of the North American Actuarial Journal and Associate Editor for Insurance: Mathematics and Economics. An award-winning researcher, he as published in the leading refereed academic journals in Business and Economics and Theoretical and Applied Statistics.

Inhaltsangabe

1. Introduction
Part I. Linear Models: 2. Fixed effects models
3. Models with random effects
4. Prediction and Bayesian Inference
5. Multilevel models
6. Random regressors
7. Modeling issues
8. Dynamic models
Part II. Nonlinear Models: 9. Binary dependent variables
10. Generalized linear models
11. Categorical dependent variables and survival models
Appendix A. Elements of Matrix Algebra
Appendix B. Normal distribution
Appendix C. Likelihood-based inference
Appendix D. Kalman Filter
Appendix E. Symbols and notation
Appendix F. Selected longitudinal and panel data sets
Appendix G. References.