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International health spending forecasts: Concepts and evaluation

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  • Getzen, Thomas E.
  • Poullier, Jean-Pierre

Abstract

Health care depends on the organizational and financial decisions which constituted each national system. Since those decisions were made at various times over the preceding years under different macroeconomic conditions, current expenditures are a distributed lag function of GDP growth and inflation rates. The accuracy of forecasts from such causal econometric models are compared to exponential smoothing, moving average, and ARIMA methods. Data for 19 OECD countries 1965-79 are used for calibration, and then ex ante forecasts are generated for 1980-87 so that actual forecast accuracy can be tested. The greatest reduction in mean absolute error was obtained with the econometric model estimated in aggregate across all 19 countries, although single-country models, exponential smoothing and international averaging were also effective. A combination of all four forecasts was more accurate than any one alone, reducing MAE by 25% relative to a constant growth projection.

Suggested Citation

  • Getzen, Thomas E. & Poullier, Jean-Pierre, 1992. "International health spending forecasts: Concepts and evaluation," Social Science & Medicine, Elsevier, vol. 34(9), pages 1057-1068, May.
  • Handle: RePEc:eee:socmed:v:34:y:1992:i:9:p:1057-1068
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    Citations

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    Cited by:

    1. Badi H. Baltagi & Raffaele Lagravinese & Francesco Moscone & Elisa Tosetti, 2017. "Health Care Expenditure and Income: A Global Perspective," Health Economics, John Wiley & Sons, Ltd., vol. 26(7), pages 863-874, July.
    2. Getzen, Thomas E., 2000. "Health care is an individual necessity and a national luxury: applying multilevel decision models to the analysis of health care expenditures," Journal of Health Economics, Elsevier, vol. 19(2), pages 259-270, March.
    3. Jennifer Roberts, 1999. "Sensitivity of elasticity estimates for OECD health care spending: analysis of a dynamic heterogeneous data field," Health Economics, John Wiley & Sons, Ltd., vol. 8(5), pages 459-472, August.
    4. Zeynep Ceylan & Abdulkadir Atalan, 2021. "Estimation of healthcare expenditure per capita of Turkey using artificial intelligence techniques with genetic algorithm‐based feature selection," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 279-290, March.
    5. Joan Costa-Font & Marin Gemmill & Gloria Rubert, 2008. "Re-visiting the Health Care Luxury Good Hypothesis: Aggregation, Precision, and Publication Biases?," Working Papers in Economics 197, Universitat de Barcelona. Espai de Recerca en Economia.
    6. Barik, Debasis & Arokiasamy, Perianayagam, 2016. "Rising Health Expenditure Due to Non-Communicable Diseases in India: An Outlook," MPRA Paper 77223, University Library of Munich, Germany, revised 15 Nov 2016.
    7. Thomas E. Getzen & Albert A. Okunade, 2017. "Symposium Introduction: Papers on ‘Modeling National Health Expenditures’," Health Economics, John Wiley & Sons, Ltd., vol. 26(7), pages 827-833, July.
    8. Marwa Farag & A. NandaKumar & Stanley Wallack & Dominic Hodgkin & Gary Gaumer & Can Erbil, 2012. "The income elasticity of health care spending in developing and developed countries," International Journal of Health Economics and Management, Springer, vol. 12(2), pages 145-162, June.
    9. Astolfi, Roberto & Lorenzoni, Luca & Oderkirk, Jillian, 2012. "Informing policy makers about future health spending: A comparative analysis of forecasting methods in OECD countries," Health Policy, Elsevier, vol. 107(1), pages 1-10.
    10. Michael Kendix & Thomas E. Getzen, 1994. "US health services employment: A time series analysis," Health Economics, John Wiley & Sons, Ltd., vol. 3(3), pages 169-181, May.
    11. DRITSAKIS, Nikolaos & KLAZOGLOU, Paraskevi, 2019. "Time Series Analysis using ARIMA Models: An Approach to Forecasting Health Expenditure in USA," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 72(1), pages 77-106.

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