Regression Modeling with Actuarial and Financial Applications

This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.

Edward W. Frees (Author)

9780521135962, Cambridge University Press

Paperback, published 30 November 2009

584 pages, 139 b/w illus. 142 tables 89 exercises
24.8 x 17.4 x 3 cm, 0.95 kg

'It would be an ideal text for a semester - or a year-long course in applied statistical methods for actuarial science majors. But it would also be a welcome addition to the bookshelf of pracitcing actuaries at all levels, both actuarial students charged with conducting analyses for which the methods discussed in the book are most relevant, and senior managers who use such analysis as a basis for financial decision making … Perhaps my favorite part of Fee's book is the final two chapters, on Report Writing and Designing Effective Graphs. If these fine essays do not already appear somewhere on the Society of Actuaries syllabus, they should be added immediately.' Ronald C. Neath, The American Statistician

This text gives budding actuaries and financial analysts a foundation in multiple regression and time series. They will learn about these statistical techniques using data on the demand for insurance, lottery sales, foreign exchange rates, and other applications. Although no specific knowledge of risk management or finance is presumed, the approach introduces applications in which statistical techniques can be used to analyze real data of interest. In addition to the fundamentals, this book describes several advanced statistical topics that are particularly relevant to actuarial and financial practice, including the analysis of longitudinal, two-part (frequency/severity), and fat-tailed data. Datasets with detailed descriptions, sample statistical software scripts in 'R' and 'SAS', and tips on writing a statistical report, including sample projects, can be found on the book's Web site: http://research.bus.wisc.edu/RegActuaries.

1. Regression and the normal distribution
Part I. Linear Regression: 2. Basic linear regression
3. Multiple linear regression - I
4. Multiple linear regression - II
5. Variable selection
6. Interpreting regression results
Part II. Topics in Time Series: 7. Modeling trends
8. Autocorrelations and autoregressive models
9. Forecasting and time series models
10. Longitudinal and panel data models
Part III. Topics in Nonlinear Regression: 11. Categorical dependent variables
12. Count dependent variables
13. Generalized linear models
14. Survival models
15. Miscellaneous regression topics
Part IV. Actuarial Applications: 16. Frequency-severity models
17. Fat-tailed regression models
18. Credibility and bonus-malus
19. Claims triangles
20. Report writing: communicating data analysis results
21. Designing effective graphs
Appendix 1: basic statistical inference
Appendix 2: matrix algebra
Appendix 3: probability tables.

Subject Areas: Probability & statistics [PBT], Insurance & actuarial studies [KFFN]