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Satellite Data for Nowcasting : Estimating Cambodia’s GDP in Real Time Using Satellite Data in a Machine Learning Framework / Iyke Maduako, Dharana Rijal, Alberto Sanchez Rodelgo.

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Format:
Book
Government document
Author/Creator:
Maduako, Iyke.
Contributor:
Rijal, Dharana.
Sanchez Rodelgo, Alberto.
Series:
Selected Issues Papers; Selected Issues Paper ; No. 2026/001
Selected Issues Papers
Language:
English
Physical Description:
1 online resource (12 pages)
Place of Publication:
Washington, D.C. : International Monetary Fund, 2026.
Summary:
Cambodia is not alone in facing capacity limitations in the production and timely release of key official statistics needed for data-driven policy decisions. This paper demonstrates that combining satellite-derived indicators (e.g., nighttime lights, NO₂ emissions, vegetation indices) with traditional high-frequency indicators in a machine learning framework significantly improves the accuracy of GDP nowcasts. Moreover, satellite data enables closer examination of subnational patterns, providing granular, near-real-time insights into economic activity. These findings highlight the potential of non-traditional approaches to complement conventional methods and strengthen macroeconomic surveillance in data-scarce environments.
Notes:
Description based on print version record.
ISBN:
979-82-290-3533-0

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