Nowcasting GCC GDP : A Machine Learning Solution for Enhanced Non-Oil GDP Prediction / Greta Polo, Yuan Gao Rollinson, Yevgeniya Korniyenko, Tongfang Yuan.

Format:

Book

Government document

Author/Creator:

Polo, Greta.

Contributor:

Korniyenko, Yevgeniya.

Rollinson, Yuan Gao.

Yuan, Tongfang.

Series:

IMF Working Papers; Working Paper ; No. 2025/268

IMF Working Papers

Language:

English

Physical Description:

1 online resource (36 pages)

Place of Publication:

Washington, D.C. : International Monetary Fund, 2025.

Summary:

This paper presents a machine learning–based nowcasting framework for estimating quarterly non-oil GDP growth in the Gulf Cooperation Council (GCC) countries. Leveraging machine learning models tailored to each country, the framework integrates a broad range of high-frequency indicators—including real activity, financial conditions, trade, and oil-related variables—to produce timely, sector-specific estimates. Advancing the nowcasting literature for the MENA region, this approach moves beyond single-model methodologies by incorporating a richer set of high-frequency, cross-border indicators. It presents two key innovations: (i) a tailored data integration strategy that broadens and automates the use of high-frequency indicators; and (ii) a novel application of Shapley value decompositions to enhance model interpretability and guide the iterative selection of predictive indicators. The framework’s flexibility allows it to account for the region’s unique economic structures, ongoing reform agendas, and the spillover effects of oil market volatility on non-oil sectors. By enhancing the granularity, responsiveness, and transparency of short-term forecasts, the model enables faster, data-driven policy decisions strengthening economic surveillance and enhancing policy agility across the GCC amid a rapidly evolving global environment.

Notes:

Description based on print version record.

ISBN:

979-82-290-3457-9