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Western Hemisphere > Argentina

You are looking at 1 - 2 of 2 items for :

  • Type: Journal Issue x
  • Data Collection and Data Estimation Methodology; Computer Programs: General x
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Cover IMF Working Papers
Predicting IMF-Supported Programs: A Machine Learning Approach
Tsendsuren Batsuuri
,
Shan He
,
Ruofei Hu
,
Jonathan Leslie
, and
Flora Lutz
This study applies state-of-the-art machine learning (ML) techniques to forecast IMF-supported programs, analyzes the ML prediction results relative to traditional econometric approaches, explores non-linear relationships among predictors indicative of IMF-supported programs, and evaluates model robustness with regard to different feature sets and time periods. ML models consistently outperform traditional methods in out-of-sample prediction of new IMF-supported arrangements with key predictors that align well with the literature and show consensus across different algorithms. The analysis underscores the importance of incorporating a variety of external, fiscal, real, and financial features as well as institutional factors like membership in regional financing arrangements. The findings also highlight the varying influence of data processing choices such as feature selection, sampling techniques, and missing data imputation on the performance of different ML models and therefore indicate the usefulness of a flexible, algorithm-tailored approach. Additionally, the results reveal that models that are most effective in near and medium-term predictions may tend to underperform over the long term, thus illustrating the need for regular updates or more stable – albeit potentially near-term suboptimal – models when frequent updates are impractical.
Keywords:
Early warning systems; IMF Lending; Machine Learning; IMF arrangement; ML model; machine learning approach; state-of-the-art machine learning; machine learning model; Emergency assistance; Data processing; Global financial crisis of 2008-2009; Global
Cover Behind the Scenes with Data at the IMF
Behind the Scenes with Data at the IMF
An IEO Evaluation
International Monetary Fund. Independent Evaluation Office

Abstract

This report examines whether the IMF has effectively leveraged an important asset: data. It finds that in general, the IMF has been able to rely on a large amount of data of acceptable quality, and that data provision from member countries has improved markedly over time. Nonetheless, problems with data or data practices have, at times, adversely affected the IMF’s surveillance and lending activities. The roots of data problems are diverse, ranging from problems due to member countries’ capacity constraints or reluctance to share sensitive data to internal issues such as lack of appropriate staff incentives, institutional rigidities, and long-standing work practices. Efforts to tackle these problems are piecemeal, the report finds, without a clear comprehensive strategy that recognizes data as an institutional strategic asset, not just a consumption good for economists. The report makes a number of recommendations that could promote greater progress in this regard.

Keywords:
IEOR; IEO; data; data management; evaluation; data practice; IMF data provision; model data management guideline; data management framework; data deficiency; Special Data Dissemination Standard (SDDS); Data dissemination; Financial statistics; Financial statements; Global; data challenge; data management initiative; evaluation team; IMF document; Data collection