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Corresponding Author:
Nicholaos Dritsakis, Department of Applied Informatics, University of Macedonia, Economics and Social Sciences, Thessaloniki, Greece

Coauthors:
Paraskevi Klazoglou, Department of Applied Informatics, University of Macedonia, Economics and Social Sciences, Thessaloniki, Greece

Time Series Analysis using ARIMA Models: An Approach to Forecasting Health Expenditures in USA

Volume 72 - Issue 1, February 2019
(pp. 77-106)
JEL classification: C53, E27
Keywords: ARIMA Model, Health Expenditure, Box-Jenkins, Forecasting

Abstract

Many OECD countries are at the heart of the political agenda regarding rising healthcare spending and its long-term sustainability. The continuous rise in health expenditure exerts pressure on government budgets, health services and personal patient finance. This has led policy makers to implement reforms in order to mitigate pressures on these costs, as well as introduce programs and forecasting models to provide a support tool capable of adapting to issues that may arise. The purpose of this study is to investigate the best model to predict total health spending in the USA, a country with the highest global spending, using the Box-Jenkins methodology. Applying annual data for total US health expenditure from 1900 to 2017, resulted in the ARIMA (2,1,0) model with static forecasting being the most appropriate to predict these costs. Model estimation was achieved by the maximum likelihood-ML method and finally, the accuracy of the forecast was assessed based on certain criteria such as the root mean square error (RMSE), mean absolute percentage error (MAPE) and Theil’s inequality coefficient.

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Institute for International Economics of the Genoa Chamber of Commerce

Institute for International Economics
of the Genoa Chamber of Commerce