Description: The projection methods used by the Social Security Administration's Office of the Chief Actuary are important. The resulting projections are cited regularly by policy-makers, academics and others interested in the long-term financial solvency of the OASDI system. However, a complete description of this projection methodology that is vital for serious policy discussions is not available to the general public. This summary was constructed in connection with the Social Security Advisory Board's 1999 Technical Panel on Methods and Assumptions. A goal of the Methods and Modeling working group of the Technical Panel was to 'work to ensure that the projection methods used by the Office of the Chief Actuary become more public.' From Actuarial Research Clearing House 2000, Vol. 1.

Description: Abstract for a paper that develops links between credibility theory in actuarial science and longitudinal data models in statistics. The paper demonstrates that many additive credibility models can be expressed as special cases of the longitudinal data model.

Description: This paper investigates the use of models of dependent mortality for determining annuity values. We discuss a broad class of parametric models using a bivariate survivorship function called a copula. Using data from a large insurance company to illustrate our methods, we calculate maximum likelihood estimates to calibrate the rnodel.

Description: This paper introduces actuaries to the concept of 'copulas,' a tool for understanding relationships among multivariate outcomes. A copula is a function that links univariate marginals to their full multivariate distribution. Recently, there has been a rapidly developing literature on the statistical properties and applications of copulas. This paper explores some of these practical applications, including estimation of joint life mortality and multidecrement models. In addition, the paper describes basic properties of copulas, their relationships to measures of dependence and several families of copulas that have appeared in the literature. This paper will also be useful to those who wish to use copulas for statistical inference. Statistical inference procedures are illustrated using insurance company data on losses and expenses. For this data, the paper shows how to fit copulas and describes their usefulness by pricing a reinsurance contract and estimating expenses for pre-specified losses.