The IMF Working Papers series is designed to make IMF staff research available to a wide audience. Almost 300 Working Papers are released each year, covering a wide range of theoretical and analytical topics, including balance of payments, monetary and fiscal issues, global liquidity, and national and international economic developments.
Model selection and forecasting in stress tests can be facilitated using machine learning
techniques. These techniques have proved robust in other fields for dealing with the curse of
dimensionality, a situation often encountered in applied stress testing. Lasso regressions, in
particular, are well suited for building forecasting models when the number of potential
covariates is large, and the number of observations is small or roughly equal to the number of
covariates. This paper presents a conceptual overview of lasso regressions, explains how they
fit in applied stress tests, describes its advantages over other model selection methods, and
illustrates their application by constructing forecasting models of sectoral probabilities of
default in an advanced emerging market economy.