Our comprehensive study on UAE GDP forecasting evaluates the effectiveness of three popular econometric models: ARIMA, VAR, and Linear Regression. Using real GDP data, the paper highlights each model's strengths and limitations over short-term and long-term horizons. The findings suggest that ARIMA is most effective for long-term forecasting, while Linear Regression shines in short-term, scenario-based predictions, provided accurate exogenous variable forecasts are available. Dive into this publication to gain valuable insights into forecasting methodologies that can enhance decision-making for businesses and policymakers in the UAE's dynamic economic landscape.
Forecasting GDP is crucial for economic planning and policymaking. This study compares the performance of three widely-used econometric models—ARIMA, VAR, and Linear Regression—using GDP data from the UAE. Employing a rolling forecast approach, we analyze the models’ accuracy over different time horizons. Results indicate ARIMA’s robust long-term forecasting capability, LR models perform better with short-term predictions, particularly when exogenous variable forecasts are accurate. These insights provide a valuable foundation for selecting forecasting models in the UAE’s evolving economy, suggesting ARIMA’s suitability for long-term outlooks and LR for short-term, scenario-based forecasts.
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