This title focuses on models and data that arise from repeated observations of a cross-section of individuals, households or firms. 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. The applications are enhanced by real-world data sets and software programs in SAS and Stata.
This title focuses on models and data that arise from repeated observations of a cross-section of individuals, households or firms. 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. The applications are enhanced by real-world data sets and software programs in SAS and Stata.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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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