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<p>The study explores the application of ARIMA (Autoregressive Integrated Moving Average) modeling to forecast unemployment trends in Somalia, using quarterly data sourced from the World Bank. The research begins by addressing the non-stationarity of the original unemployment data series through first-order differencing, making it suitable for ARIMA modeling. After evaluating various models, ARIMA (1,1,3) was identified as the most appropriate based on its lower Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, indicating a good balance between fit and simplicity. Diagnostic tests confirmed the model’s stability and the randomness of residuals, though the analysis acknowledges that external factors not captured by the model could influence unemployment trends. The findings suggest that while ARIMA modeling is effective for short-term forecasting, future research should explore hybrid models and integrate external variables to enhance predictive accuracy. The study’s results provide valuable insights for policymakers in Somalia, offering a data-driven approach to anticipate and address unemployment challenges.</p>

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Understanding unemployment trends in Somalia through ARIMA modeling: insights for shaping economic policy

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Abdisalan Aden Mohamed,
  • Abdiaziz Mohamed Abdirahman,
  • Ibrahim Hussein Ahmed

摘要

The study explores the application of ARIMA (Autoregressive Integrated Moving Average) modeling to forecast unemployment trends in Somalia, using quarterly data sourced from the World Bank. The research begins by addressing the non-stationarity of the original unemployment data series through first-order differencing, making it suitable for ARIMA modeling. After evaluating various models, ARIMA (1,1,3) was identified as the most appropriate based on its lower Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, indicating a good balance between fit and simplicity. Diagnostic tests confirmed the model’s stability and the randomness of residuals, though the analysis acknowledges that external factors not captured by the model could influence unemployment trends. The findings suggest that while ARIMA modeling is effective for short-term forecasting, future research should explore hybrid models and integrate external variables to enhance predictive accuracy. The study’s results provide valuable insights for policymakers in Somalia, offering a data-driven approach to anticipate and address unemployment challenges.