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Using simulation-based inference with panel data in health economics.

Paul Contoyannis1, Andrew M Jones, Roberto Leon-Gonzalez

  • 1Centre for Health Economics and Policy Analysis, McMaster University, UK.

Health Economics
|January 23, 2004
PubMed
Summary
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Panel data econometrics for health economics presents challenges with categorical outcomes. Simulation-based estimators overcome limitations of conventional methods for analyzing individual behavior and health dynamics.

Area of Science:

  • Econometrics
  • Health Economics
  • Biostatistics

Background:

  • Panel datasets are valuable for health economics research, enabling control for individual heterogeneity and modeling behavioral dynamics.
  • Categorical outcome measures in health economics pose estimation challenges for econometric models.
  • Flexible modeling of autocorrelation due to heterogeneity leads to complex integrals intractable for conventional methods.

Purpose of the Study:

  • To review conventional econometric methods for panel data with categorical outcomes in health economics.
  • To demonstrate how simulation-based estimators expand the range of applicable models.
  • To illustrate practical applications using health data from the British Household Panel Survey (BHPS).

Main Methods:

  • Review of binary choice models in econometrics.

Related Experiment Videos

  • Application of conventional numerical integration methods.
  • Implementation and evaluation of simulation-based estimators.
  • Analysis of health outcomes using the British Household Panel Survey (BHPS).

    • Main Results:

    • Conventional methods are limited in handling complex models arising from flexible autocorrelation specifications.
    • Simulation-based estimators effectively address higher-order integrals, expanding modeling capabilities.
    • The study demonstrates the practical utility of these methods with real-world health economics data.

    Conclusions:

    • Simulation methods offer a powerful solution for econometric modeling with panel data and categorical outcomes in health economics.
    • These advanced techniques enhance the ability to analyze individual heterogeneity and behavioral dynamics.
    • The findings support the broader adoption of simulation-based estimators in health economic research.