Implementation of the ARIMA model for prediction of economic variables: evidence from the health sector in Brazil

Claudimar Pereira da Veiga (), Cássia Rita Pereira da Veiga, Felipe Mendes Girotto, Diego Antonio Bittencourt Marconatto and Zhaohui Su
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Claudimar Pereira da Veiga: Fundação Dom Cabral
Cássia Rita Pereira da Veiga: Federal University of Minas Gerais (UFMG)
Felipe Mendes Girotto: Lothário Meissner
Diego Antonio Bittencourt Marconatto: Fundação Dom Cabral
Zhaohui Su: Southeast University

Palgrave Communications, 2024, vol. 11, issue 1, 1-11

Abstract: Abstract In recent decades, quantitative models for forecasting economic crises have garnered significant interest from researchers, policymakers, and public and private institutions. Identifying the most appropriate models for predicting economic time series behaviors during crises is a pressing challenge. Effective techniques can be instrumental in forestalling financial irregularities, thus empowering institutions to deploy remedial actions and swiftly minimizing further economic setbacks. Contemporary literature introduces various forecasting models, such as the autoregressive integrated moving average (ARIMA) model. Recognized for its statistical alignment with numerous linear models, the ARIMA model has demonstrated its efficacy across various domains. This paper delves into applying the ARIMA model to predict five critical economic time series that substantially influenced Brazil’s public and private healthcare sectors throughout the economic crisis between 2000 and 2020. These time series encompassed the variables (i) the gross domestic product—GDP, (ii) the Extended National Consumer Price Index—IPCA, (iii) the unemployment rate, (iv) the total number of health plan beneficiaries, and (v) total number of individual health plan beneficiaries. Importantly, this study provides a comprehensive outline of the ARIMA implementation process, underscoring that precise forecasting is pivotal for managers aiming to curtail financial anomalies and avert resource shortages. The findings highlight the ARIMA model’s (1, 0, 2), (2, 2, 1), (0, 1, 2), (1, 1, 2), and (2, 2, 1) viability in accurately forecasting health-related time series, exceeding 95% accuracy for economic variables analyzed. These results have significant practical implications for healthcare managers and decision-makers. By offering accurate forecasts of critical economic metrics, such as the unemployment rate and the transition of beneficiaries between public and private health systems during economic downturns, this research provides valuable insights for strategic planning within the healthcare sector.

Date: 2024
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DOI: 10.1057/s41599-024-03023-3

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