Modeling and Forecasting the Real Effective Exchange Rate in Morocco: A Comparative Analysis by ARIMA, Random Forest and the Dynamic Factor Model

Souad Baya (), Abdellali Fadlallah, Hamza El Baraka (), Khalil Bourouis and Majdouline Ezzraouli
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Abdellali Fadlallah: INSEA - Institut National de Statistique et d’Economie Appliquée [Rabat]
Hamza El Baraka: UM5 - Université Mohammed V de Rabat [Agdal], INSEA - Institut National de Statistique et d’Economie Appliquée [Rabat]

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Abstract: This paper presents a comparative empirical analysis of three modeling approaches applied to the forecasting of Morocco's Real Effective Exchange Rate (REER): the ARIMA model, the Random Forest algorithm, and the Dynamic Factor Model (DFM). Utilizing a comprehensive macroeconomic quarterly dataset spanning from 1999Q4 to 2021Q3, the study assesses the out-of-sample predictive performance of these models over a structurally dynamic period, including the transition to a more flexible exchange rate regime in 2018 and the global shock induced by the COVID-19 pandemic. The findings reveal that the Random Forest model significantly outperforms both ARIMA and DFM in terms of accuracy and adaptability to structural breaks. Variable importance analysis highlights the dominant role of real economic fundamentals, particularly industrial value added, inflation, and exports in explaining REER movements. In contrast, the ARIMA model underreacts to exogenous shocks due to its univariate structure, while the DFM suffers from a loss of predictive power likely caused by excessive dimensionality reduction.

Keywords: real effective exchange rate; exchange rate forecasting; random forest; ARIMA; dynamic factor model; machine learning; structural breaks; Morocco; macroeconomic modeling (search for similar items in EconPapers)
Date: 2025-03-07
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Published in Engineering Proceedings, 2025, Advances in Science, Technology & Innovation, pp.53. ⟨10.3390/engproc2025112053⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05345285

DOI: 10.3390/engproc2025112053

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