ARIMA models are a valid approach for forecasting economic variables
ARIMA models are widely validated as a viable univariate approach for forecasting economic variables, though multivariate models or machine learning enhancements are sometimes preferred for capturing complex cross-variable correlations.
The claim that ARIMA models are a valid approach for forecasting economic variables is well supported in the literature. Multiple studies demonstrate their successful application in forecasting macroeconomic indicators such as GDP and financial assets. While comparative studies indicate that multivariate models like VAR or machine learning hybrids can sometimes achieve better performance by incorporating additional drivers or correlations, ARIMA remains a standard, effective baseline and valid standalone method.
Abas Omar Mohamed. Modeling and Forecasting Somali Economic Growth Using ARIMA Models. 2022. https://doi.org/10.3390/forecast4040056
Paper [0] demonstrates that an ARIMA model effectively forecasts Somali GDP growth with stable and statistically sound diagnostics.
M. Khan, Umama Khan. Comparison of Forecasting Performance with VAR vs. ARIMA Models Using Economic Variables of Bangladesh. 2020. https://doi.org/10.9734/ajpas/2020/v10i230243
Paper [1] finds that while ARIMA performs adequately for low-correlated variables, multivariate vector autoregression (VAR) models yield superior forecasts for highly correlated economic indicators.
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Zhaozhi Wang. The Effectiveness of Forecasting Gold Prices Using ARIMA Models. 2025. https://doi.org/10.54254/2754-1169/2025.lh23823
Paper [4] shows that ARIMA models successfully capture and predict the trends of financial assets like gold prices under stable economic conditions.
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