We develop early warning models for financial crisis prediction using machine learning techniques on macrofinancial data for 17 countries over 1870-2016. Machine learning models mostly outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of our machine learning models using a novel framework based on Shapley values, uncovering non-linear relationships between the predictors and crisis risk. Throughout, the most important predictors are credit growth and the slope of the yield curve, both domestically and globally. A flat or inverted yield curve is of most concern when nominal interest rates are low and credit growth is high.

Rules for building formal models that use fast-and-frugal heuristics, extending the psychological study of classification to the real world of uncertainty. This book focuses on classification—allocating objects into categories—“in the wild,” in real-world situations and far from the certainty of the lab. In the wild, unlike in typical psychological experiments, the future is not knowable and uncertainty cannot be meaningfully reduced to probability. Connecting the science of heuristics with machine learning, the book shows how to create formal models using classification rules that are simple, fast, and transparent and that can be as accurate as mathematically sophisticated algorithms developed for machine learning. The authors—whose individual expertise ranges from empirical psychology to mathematical modeling to artificial intelligence and data science—offer real-world examples, including voting, HIV screening, and magistrate decision making; present an accessible guide to inducing the models statistically; compare the performance of such models to machine learning algorithms when applied to problems that include predicting diabetes or bank failure; and discuss conceptual and historical connections to cognitive psychology. Finally, they analyze such challenging safety-related applications as decreasing civilian casualties in checkpoints and regulating investment banks.

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We present a comprehensive comparative case study for the use of machine learning models for macroeconomics forecasting. We find that machine learning models mostly outperform conventional econometric approaches in forecasting changes in US unemployment on a 1-year horizon. To address the black box critique of machine learning models, we apply and compare two variables attribution methods: permutation importance and Shapley values. While the aggregate information derived from both approaches is broadly in line, Shapley values offer several advantages, such as the discovery of unknown functional forms in the data generating process and the ability to perform statistical inference. The latter is achieved by the Shapley regression framework, which allows for the evaluation and communication of machine learning models akin to that of linear models.