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Neural Network Forecasts of Taiwan Bureau of National Health Insurance Expenditires

Author

Listed:
  • Chin-Piao Yeh
  • Ai-Chi Hsu
  • Wei-Hsien
  • Kuang-Cheng Chai

Abstract

This study predicts the medical expenditure of national health insurance by a Back-Propagation Neural Network (BPN). Monte Carlo Simulation and Multiple Regression Analysis are used to compare the results of the BPN. Empirical results show the performance indicator modeled on BPN is the best, followed by those modeled on Multiple Regression Analysis and Monte Carlo Simulation. In estimating the opportunity cost that will be lost when the forecasting model overestimates the expenditure, and the resource cost that will occur when the model underestimates the expenditure, the Monte Carlo Simulation and Multiple Regression Analysis are likely to be better forecasting methods. Finally, the three key factors affecting medical expenditure are the aging population index, the inflation rate and the number of insured population. This study makes a contribution extant literature by using a BPN to predict the medical expenditure performance indicator error rate. A BPN is better than other models in terms of TIC, but may not be the best forecasting method in a variety of cost conditions.

Suggested Citation

  • Chin-Piao Yeh & Ai-Chi Hsu & Wei-Hsien & Kuang-Cheng Chai, 2014. "Neural Network Forecasts of Taiwan Bureau of National Health Insurance Expenditires," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 8(5), pages 95-114.
    • Handle: RePEc:ibf:ijbfre:v:8:y:2014:i:5:p:95-114
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    References listed on IDEAS

    as
    1. Kreng, Victor B. & Yang, Chi-Tien, 2011. "The equality of resource allocation in health care under the National Health Insurance System in Taiwan," Health Policy, Elsevier, vol. 100(2), pages 203-210.
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    5. Hitiris, Theo & Posnett, John, 1992. "The determinants and effects of health expenditure in developed countries," Journal of Health Economics, Elsevier, vol. 11(2), pages 173-181, August.
    6. Van de ven, Wynand P.M.M. & Ellis, Randall P., 2000. "Risk adjustment in competitive health plan markets," Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 14, pages 755-845, Elsevier.
    7. Helmut Herwartz & Bernd Theilen, 2003. "The determinants of health care expenditure: testing pooling restrictions in small samples," Health Economics, John Wiley & Sons, Ltd., vol. 12(2), pages 113-124, February.
    8. Kreng, Victor B. & Yang, Chi-Tien, 2011. "The equality of resource allocation in health care under the National Health Insurance System in Taiwan," Health Policy, Elsevier, vol. 100(2-3), pages 203-210, May.
    9. Hamid, Shaikh A. & Iqbal, Zahid, 2004. "Using neural networks for forecasting volatility of S&P 500 Index futures prices," Journal of Business Research, Elsevier, vol. 57(10), pages 1116-1125, October.
    10. Gerdtham, Ulf-G. & Sogaard, Jes & Andersson, Fredrik & Jonsson, Bengt, 1992. "An econometric analysis of health care expenditure: A cross-section study of the OECD countries," Journal of Health Economics, Elsevier, vol. 11(1), pages 63-84, May.
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    Cited by:

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    2. Krzysztof Kluza, 2017. "Risk assessment of the local government sector based on the ratio analysis and the DEA method. Evidence from Poland," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 7(3), pages 329-351, December.

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    More about this item

    Keywords

    Medical Expenditure Forecasting; Back-Propagation Neural Network (BPN); Monte Carlo Simulation; Multiple Regression Analysis;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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