Identification of Threats to the Resilience of an Economic System Using Machine Learning and Probit Model

The purpose of this article is to identify the mechanism of shock diagnosis and to identify factors that facilitate or exacerbate the threat to resilience of economic system. Methodology is based on the data for 1993–1923 from 60 countries, four machine learning algorithms to diagnose shocks (kNN, AdaBoost, Random Forest, and MLP Neural Network). Econometric analysis was accomplished using a probit model. As a result, the research has revealed strong diagnostic abilities of AdaBoost and Random Forest algorithms for diagnosing shocks. It has also identified the influence of the studied factors on the strengthening or mitigation of the probability of a shock. Practical significance: the results of this study can be used to diagnose the resilience of territories, as well as to develop models for predicting the resilience of economic systems and developing machine tools for analysing the resilience of territories.

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