Implementation of the Arima model for prediction of economic variables
Evidence from the health sector in Brazil
Bibliographic Data
| ID | 22235439 |
|---|---|
| Authors | Claudimar Pereira Da Veiga (0000-0002-4960-5954, Universidade Federal de Minas Gerais, corresponding author), Cássia Rita Pereira da Veiga (0000-0002-9219-6279, Universidade Federal de Minas Gerais, corresponding author), Felipe Mendes Girotto (0009-0003-6685-0788, Universidade Federal do Paraná), Diego Antonio Bittencourt Marconatto (0000-0002-9458-9199, Fundação Dom Cabral), Zhaohui Su (0000-0003-2005-9504, Southeast University) |
| Year | 2024 |
| Volume | 11 |
| Issue | 1 |
| Publication date | 2024-08-22 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Humanities and Social Sciences Communications (JOURNAL) |
| Journal identifiers | ISSN: 2662-9992 • E-ISSN: 2662-9992 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1057/s41599-024-03023-3 |
| OpenAlex | W4401813085 |
| Language | EN |
| Citations received | 2 |
| References cited | 32 |
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
Actuarial science · Autoregressive integrated moving average · Box–Jenkins · Business · Econometrics · Economic growth · Economics · Gross domestic product · Health care · Statistics · Time series · Climate Change and Health Impacts · Computer Science · Global Health Care Issues · Healthcare Systems and Reforms · Mathematics
| Unique citing works | 2 |
|---|---|
| Citations per year | 1 |
| Citation span | 2024 - 2025 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |