7/31/2019 VEE Statistics

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Syllabus for the Transitional Exam for

VEE-Applied Statistical Methods

This 90-minute, multiple-choice examination is administered by the CAS to satisfy the Validation by

Educational Experience (VEE)Applied Statistical Methods requirement for candidates who have not

taken the required courses. The first administration of this exam will occur in August 2005. Details about

the 2005 administration, including the date and exam fee, will be posted in the Admissions section ofthe CAS Web Site (www.casact.org) in November 2004.

LEARNING OBJECTIVES

The candidate is expected to demonstrate an understanding of the terminology and underlying

assumptions of regression and time series models, and to be able to apply and analyze an appropriately

selected model when solving insurance related problems. Specifically, the candidate is expected to be

able to perform the tasks listed in the learning objectives below.

A. Regression1. Estimate the parameters of linear regression models.

2. Test hypotheses and construct confidence intervals for the parameters of linear regression

models.3. Determine the appropriateness of a regression model by analyzing residuals and applying the F-

test.

4. Calculate elasticities and partial correlations.

5. Apply appropriate measures when the data is observed to possess one or more of the following

characteristics:a. Heteroscedasticity

b. Serial correlation

c. Multicollinearity

d. Nonlinearity

6. Estimate and determine confidence intervals for future observations using linear regression

models.

7. Demonstrate familiarity with inherently nonlinear regression models and set up equations that

would be used in estimating parameters of such models.B. Time Series

1. Distinguish between regression and time series models.

2. Distinguish between and apply deterministic and stochastic time series models.

3. Recognize characteristics of stationary time series and compute autocorrelation functions.

4. Analyze data using a random walk model.

5. Estimate the parameters of ARIMA models, and the simpler models AR, MA, and ARMA as

special cases.6. Run diagnostic checks to validate a specified time series model.

7. Generate forecasts using ARIMA, and simpler, models and develop confidence intervals for the

forecasts.8. Demonstrate familiarity with the properties of ARIMA forecasts.

READINGS

Pindyck, R.S.; and Rubinfeld, D.L.,Econometric Models and Economic Forecasts (Fourth

Edition), 1998, Irwin McGraw-Hill, Boston, Chapters 1, 3, 4, 5, 6 (excluding Appendix 6.1),

Sections 8.1, 8.2, 10.1, Chapters 15, 16 (excluding Appendix 16.1), 17 (excluding Appendix17.1), and 18.