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Predicting quantity and quality of life with the Future Elderly Model

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Listed:
  • Duncan Ermini Leaf
  • Bryan Tysinger
  • Dana P. Goldman
  • Darius N. Lakdawalla

Abstract

The Future Elderly Model (FEM) is a microsimulation model designed to forecast health status, longevity, and a variety of economic outcomes. Compared to traditional actuarial models, microsimulation models provide greater opportunities for policy forecasting and richer detail, but they typically build upon smaller samples of data that may mitigate forecasting accuracy. We perform validation analyses of the FEM's mortality and quality of life forecasts using a version of the FEM estimated exclusively on early waves of data from the Health and Retirement Study. First, we compare FEM mortality and longevity projections to the actual mortality and longevity experience observed over the same period of time. We also compare the FEM results to actuarial forecasts of mortality and longevity during the same time. We find that FEM projections are generally in line with observed mortality rates and closely match longevity. Then, we assess the FEM's performance at predicting quality of life and longitudinal outcomes, two features missing from traditional actuarial models. Our analysis suggests the FEM performs at least as well as actuarial forecasts of mortality, while providing policy simulation features that are not available in actuarial models.

Suggested Citation

  • Duncan Ermini Leaf & Bryan Tysinger & Dana P. Goldman & Darius N. Lakdawalla, 2021. "Predicting quantity and quality of life with the Future Elderly Model," Health Economics, John Wiley & Sons, Ltd., vol. 30(S1), pages 52-79, November.
    • Handle: RePEc:wly:hlthec:v:30:y:2021:i:s1:p:52-79
    DOI: 10.1002/hec.4169
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    References listed on IDEAS

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    Cited by:

    1. Jorge Luis Garcia & Frederik Bennhoff & Duncan Ermini Leaf & James J. Heckman, 2021. "The Dynastic Benefits of Early Childhood Education," Working Papers 2021-033, Human Capital and Economic Opportunity Working Group.

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