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Calibration of a stochastic health evolution model using NHIS data

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  • Gupta, Aparna
  • Li, Zhisheng

Abstract

This paper presents and calibrates an individual’s stochastic health evolution model. In this health evolution model, the uncertainty of health incidents is described by a stochastic process with a finite number of possible outcomes. We construct a comprehensive health status index (HSI) to describe an individual’s health status, as well as a health risk factor system (RFS) to classify individuals into different risk groups. Based on the maximum likelihood estimation (MLE) method and the method of nonlinear least squares fitting, model calibration is formulated in terms of two mixed-integer nonlinear optimization problems. Using the National Health Interview Survey (NHIS) data, the model is calibrated for specific risk groups. Longitudinal data from the Health and Retirement Study (HRS) is used to validate the calibrated model, which displays good validation properties. The end goal of this paper is to provide a model and methodology, whose output can serve as a crucial component of decision support for strategic planning of health related financing and risk management.

Suggested Citation

  • Gupta, Aparna & Li, Zhisheng, 2011. "Calibration of a stochastic health evolution model using NHIS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3524-3540.
    • Handle: RePEc:eee:phsmap:v:390:y:2011:i:20:p:3524-3540
    DOI: 10.1016/j.physa.2011.04.003
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    References listed on IDEAS

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    Cited by:

    1. Nitin Mehta & Jian Ni & Kannan Srinivasan & Baohong Sun, 2017. "A Dynamic Model of Health Insurance Choices and Healthcare Consumption Decisions," Marketing Science, INFORMS, vol. 36(3), pages 338-360, May.
    2. Aparna Gupta & Zhisheng Li, 2013. "Optimal Annuity Purchase Decisions Under Uncertain Lifetime," Journal of Family and Economic Issues, Springer, vol. 34(4), pages 447-459, December.

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