Longitudinal and Panel Data: Analysis and Applications in the Social Sciences

by Edward W. Frees

Overview

Focusing on an analysis of models and data that arise from repeated observations of a cross-section of individuals, households or firms, this book also covers 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 social science graduate students as well as individual researchers. He emphasizes mathematical and statistical fundamentals but also demonstrates substantive applications from across the social sciences. These applications are enhanced by real-world data sets and software programs in SAS and Stata.

Product Details

ISBN-13:	9780521535380
Publisher:	Cambridge University Press
Publication date:	08/16/2004
Edition description:	New Edition
Pages:	484
Product dimensions:	5.98(w) x 9.02(h) x 1.06(d)

About the Author

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.

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.