Contents of the volume

2019, Volume 72 - Issue 1

ISSN: 2499-8265
RSS feed citation: At RePEc
Publication date: 01 February 2019

STUDY ON THE CAUSALITY NEXUS BETWEEN MACRO-ECONOMIC VARIABLES USING VECTOR ERROR CORRECTION MODELING

Shailender Singh, Hawati Janor

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REMITTANCES AND FOREIGN AIDS: SUBSTITUTES OR COMPLEMENTS IN THE ECONOMICS GROWTH OF DEVELOPING COUNTRIES?

Temitope Laniran, Victoria A. Olakunle

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FOREIGN DIRECT INVESTMENTS, EXPORTS AND ECONOMIC GROWTH IN SIDS: EVIDENCE FROM SANTA LUCIA

Roger Hosein, Regan Deonanan, Kimbert Evans

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TIME SERIES ANALYSIS USING ARIMA MODELS: AN APPROACH TO FORECASTING HEALTH EXPENDITURES IN USA

Nicholaos Dritsakis, Paraskevi Klazoglou

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THE DEMAND FOR PUBLIC AND PRIVATE MEDICAL CARE SERVICES: EVIDENCE FROM KUWAIT

Nadeem A. Burney, Nadia Al-Musallam, Mohammad Alenezi, Salwa Hamada

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Genoa Chamber of Commerce
Economia Internazionale / International Economics

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Corresponding author

Nicholaos DRITSAKIS, Department of Applied Informatics, University of Macedonia, Economics and Social Sciences, Thessaloniki, Greece

Co-authors

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

Pages

77-106

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.

JEL classification

C53, E27

Keywords

ARIMA Model, Health Expenditure, Box-Jenkins, Forecasting

Index

  1. Introduction
  2. Literature review
  3. Methodology
  4. Data and empirical results
  5. Forecasting methodology
  6. Conclusion

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