柬埔寨：选定问题

IMF Country Report No.25/318 CAMBODIA SELECTED ISSUES December 2025 This paperonCambodiawas prepared by a staff team of the International MonetaryFund as background documentation for the periodic consultation with the member Copies of this report are available to the public from International Monetary Fund•Publication ServicesPO Box 92780•Washington, D.C. 20090Telephone: (202) 623-7430•Fax: (202) 623-7201 International Monetary Fund CAMBODIA SELECTED ISSUES November 5, 2025 Approved ByAsia and Pacific Preparedby Iyke Maduako, Dharana Rijal, Alberto SanchezRodelgo, and Natasha Che. CONTENTS SATELLITE DATA FOR NOWCASTING ________________________________________________ 2 A. Motivation – Why Satellite Data _____________________________________________________ 2B. Data and Methodology_______________________________________________________________ 3C. Results and Interpretation____________________________________________________________ 7 BOX 1. Satellite Indicators to Gain Timely and Granular Insights on MacroeconomicDevelopments__________________________________________________________________________5 EXTERNAL DRIVERS OF CREDIT CYCLES IN CAMBODIA ____________________________ 10 A. Introduction ________________________________________________________________________ 10B. Data and Methodology _____________________________________________________________ 11C. Results ______________________________________________________________________________ 13D. Conclusion__________________________________________________________________________ 16 SATELLITE DATA FOR NOWCASTING A.Motivation – Why Satellite Data 1.Cambodia faces limited institutional capacity in the production and timely release ofquality official statistics, limiting policymakers’ ability to make agile and effective policydecisions.While the country has made significant improvements on the availability and quality ofnational statistics, further strengthening of statistical capacity is needed. GDP data is available onlyat annual frequency and published with a significant lag, limiting timely analysis of comprehensive 2.Satellite indicators are available for nearly all countries in the world. They exist innear-real time and at granular levels capturing nuances that may otherwise go undetected. They can serve as proxies for economic activity in various sectors of the economy. In particular, dataon nighttime lights, nitrogen dioxide (NO2) emissions, and vegetation-related indices can helpuncover underlying patterns and trends in sectors like manufacturing and agriculture.2In Cambodia,quarterly GDP growth rate (interpolated, see section on Data and Methodology) is positively 3.Machine learning models can make the best use of satellite indicators, along withmacroeconomic data to analyze their complex interactions for nowcasting GDP.First, thedataset is split into 'train' and 'test' sets. The model learns patterns based on the train set, and itspredictions are then evaluated against observed values in the test set—data that was not used B.Data and Methodology 4.The machine learning method applies quarterly satellite indicators, along with thetraditional variables, for training the nowcasting model.The satellite (“non-traditional”)indicatorsused in this analysis include data on nighttime lights (NTL), NO2 emissions (NO2),precipitation (PCP), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), •Nighttime Lights (NTL)are satellite-based measurements of the intensity of light emittedat the Earth’s surface, which is shown to be a good proxy for economic activities in many •Nitrogen dioxide (NO₂)is a pollutant, primarily produced by the combustion of fossil fuelsin power plants, industrial facilities, and vehicles. Because NO₂ is emitted in large quantities when economic activity is high, satellite-based observations of NO₂ can approximate the •Normalized Difference Vegetation Index (NDVI)andEnhanced Vegetation Index (EVI) are computed using the red (R) and near-infrared (NIR) bands of satellite imagery. Theseindices measure vegetation health and can be used to proxy agricultural output, and land •Agricultural Stress Index (ASI)is a satellite-based indicator designed to detect areas ofcropland experiencing water stress—such as drought conditions—during the growing •Vegetation Health Index (VHI)is computed using NDVI and Land Surface Temperature(LST) as inputs. First, the vegetation condition index (VCI) is derived from NDVI to assessvegetation greenness. Then, the temperature condition index (TCI) is calculated to measure •Precipitation indicator (PCP)obtained from Climate Hazards Center is InfraRed-basedprecipitation data combined with in-situ station data (CHIRPS). This is a quasi-global rainfalldataset of CHIRPS, which covers a long history (30 plus years) and incorporates 0.05°resolution satellite imagery with in-situ station data, to create gridded rainfall time series for Non-traditional and Traditiona