COVID-19

Despite the challenging external environment, the GCC economies have been resilient. Non-hydrocarbon activity has remained robust amid strong domestic demand supported by the reform momentum, limited spillovers from regional, as well as the modest direct impact of higher U.S. tariffs given the exemption of energy products and limited trade ties with the U.S. While external balances narrowed amid oil production cuts and robust imports, the external positions remain overall strong. The economic outlook remains favorable but risks are tilted to the downside amidst elevated global uncertainty. Economic activity will be supported by the unwinding of oil production cuts, the expansion of natural gas production, and strong reform and project implementation facilitated by ample policy buffers. External buffers would remain comfortable despite narrower current account balances driven by higher imports. The near-term risks to the outlook are tilted to the downside, as oil prices could decline and financial conditions tighten amid high uncertainty. Over the medium term, ongoing global structural shifts pose two-sided risks for the GCC economies.

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in nowcasting across simulation and six country cases, traditional econometric models tend to outperform ML algorithms. Among the ML algorithms, linear ML algorithm – Lasso and Elastic Net – perform best in nowcasting, even surpassing traditional econometric models in cases of long GDP data and rich high-frequency indicators. Among the traditional econometric models, the Bridge and Dynamic Factor deliver the strongest empirical results, while Three-Pass Regression Filter performs well in our simulation. Due to the relatively short length of GDP series, complex and non-linear ML algorithms are prone to overfitting, which compromises their out-of-sample performance.

This paper provides new cross-country evidence that greater investment in agricultural R&D significantly mitigates the adverse effects of climate variability on crop yields in sub-Saharan Africa. Despite this critical role, only a handful of countries have invested at levels sufficient to reach the thresholds where R&D delivers effective risk adaptation. Our analysis indicates that closing this gap would require an additional US$1–3 billion in annual agricultural research investment across the region.