Longitudinal and Panel Data
Analysis and Applications in the Social Sciences
Published on 16. August 2004
Book
Hardback
484 pages
978-0-521-82828-4 (ISBN)
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.
More details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 33 mm
Weight
930 gr
ISBN-13
978-0-521-82828-4 (9780521828284)
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Schweitzer Classification
Other editions
Additional editions
E-Book
07/2006
1st Edition
Cambridge University Press
€47.49
Available for download
Book
08/2004
Cambridge University Press
€73.10
Shipment within 15-20 days
Person
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.
Content
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.