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Forecasting medical inflation in the European Union using the ARIMA model

Author

Listed:
  • Enja Erker

    (Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia)

Abstract

As healthcare costs continue to pose significant challenges for governments and policymakers, accurate forecasting of medical inflation has become crucial in the European Union. This study aims to provide insights into the trajectory of medical inflation within the EU using the Autoregressive Integrated Moving Average (ARIMA) model and to check whether this model is an effective tool for predictions of medical inflation. The findings of the study have significant implications across various sectors. With accurate forecasts of medical inflation, policymakers can proactively address challenges, insurers can determine appropriate premiums and develop innovative models, and healthcare entities can allocate resources strategically to ensure financial stability and quality care.

Suggested Citation

  • Enja Erker, 2024. "Forecasting medical inflation in the European Union using the ARIMA model," Public Sector Economics, Institute of Public Finance, vol. 48(1), pages 39-56.
    • Handle: RePEc:ipf:psejou:v:48:y:2024:i:1:p:39-56
    DOI: 10.3326/pse.48.1.2
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    References listed on IDEAS

    as
    1. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    2. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    3. Jan J. J. Groen & Richard Paap & Francesco Ravazzolo, 2013. "Real-Time Inflation Forecasting in a Changing World," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 29-44, January.
    4. James H. Stock & Mark W. Watson, 2010. "Modeling Inflation After the Crisis," Working Papers 2010-1, Princeton University. Economics Department..
    5. M. Ali Choudhary & Adnan Haider, 2012. "Neural network models for inflation forecasting: an appraisal," Applied Economics, Taylor & Francis Journals, vol. 44(20), pages 2631-2635, July.
    6. James H. Stock & Mark W. Watson, 2010. "Modeling inflation after the crisis," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 173-220.
    7. Bradley T. Ewing & Michael J. Piette & James E. Payne, 2003. "Forecasting Medical Net Discount Rates," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 70(1), pages 85-95, March.
    8. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March.
    9. Borger, Christine & Rutherford, Thomas F. & Won, Gregory Y., 2008. "Projecting long term medical spending growth," Journal of Health Economics, Elsevier, vol. 27(1), pages 69-88, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    medical inflation; HICP; ARIMA model; time series forecasting;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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