Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models

Jorge Chan-Lau, Ruofei Hu, Maksym Ivanyna, Ritong Qu and Cheng Zhong

No 2023/041, IMF Working Papers from International Monetary Fund

Abstract: Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs model interpretability and their ability to provide adequate guidance in the design of crisis prevention and mitigation policies. This paper introduces surrogate data models as dimensionality reduction tools in large-scale crisis prediction models. The appropriateness of this approach is assessed by their application to large-scale crisis prediction models developed at the IMF. The results are consistent with economic intuition and validate the use of surrogates as interpretability tools.

Keywords: Crisis prediction; machine learning; surrogates; explainable models; IMF ML crisis; ML crisis prediction; Annex I. IMF ML; surrogate data models; model interpretability; Early warning systems; Yield curve; Deposit rates; Global (search for similar items in EconPapers)
Pages: 31
Date: 2023-02-24
New Economics Papers: this item is included in nep-big and nep-cmp
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