Comparative Analysis of ARIMA, VAR, and Linear Regression Models for UAE GDP Forecasting

Pj McCloskey and Rodrigo Malheiros Remor

MPRA Paper from University Library of Munich, Germany

Abstract: 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.

Keywords: GDP forecasting; ARIMA; VAR; Linear Regression; UAE economy (search for similar items in EconPapers)
JEL-codes: O1 O10 O4 O40 (search for similar items in EconPapers)
Date: 2024-09-10, Revised 2024-12-01
New Economics Papers: this item is included in nep-ara and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:122860

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