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20 - Transition Modeling

from IV - Longitudinal Modeling

Published online by Cambridge University Press:  05 August 2014

By
Bruce Jones  and
Weijia Wu
Edited by
Edward W. Frees ,
Richard A. Derrig  and
Glenn Meyers
Bruce Jones
Affiliation:
University of Western Ontario
Weijia Wu
Affiliation:
University of Southern California
Edward W. Frees
Affiliation:
University of Wisconsin, Madison
Richard A. Derrig
Affiliation:
Temple University, Philadelphia
Glenn Meyers
Affiliation:
ISO Innovative Analytics, New Jersey
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Summary

Chapter Preview. This chapter provides an introduction to transition modeling. Consider a situation where an individual or entity is, at any time, in one of several states and may from time to time move from one state to another. The state may, for example, indicate the health status of an individual, the status of an individual under the terms of an insurance policy, or even the “state” of the economy. The changes of state are called transitions. There is often uncertainty associated with how much time will be spent in each state and which state will be entered on each transition. This uncertainty can be modeled using a multistate stochastic model. Such a model may be described in terms of the rates of transition from one state to another. Transition modeling involves the estimation of these rates from data.

Actuaries often work with contracts involving several states and financial implications associated with presence in a state or transition between states. A life insurance policy is a simple example. A multistate stochastic model provides a valuable tool to help the actuary analyze the cash flow structure of a given contract. Transition modeling is essential to the creation of this tool.

This chapter is intended for practitioners, actuaries, or analysts who are faced with a multistate setup and need to estimate the rates of transition from available data. The assumed knowledge – only basic probability and statistics as well as life contingencies – is minimal.

Type
Chapter
Information
Predictive Modeling Applications in Actuarial Science , pp. 515 - 538
Publisher: Cambridge University Press
Print publication year: 2014

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