Regression modeling with actuarial and financial applications

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

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