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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.

Recessions. --- Lasso. --- Rope --- Bolas --- Trick roping --- Business cycles --- Depressions --- Banks and Banking --- Foreign Exchange --- Investments: General --- Macroeconomics --- Forecasting and Other Model Applications --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Price Level --- Inflation --- Deflation --- Interest Rates: Determination, Term Structure, and Effects --- General Financial Markets: General (includes Measurement and Data) --- Currency --- Foreign exchange --- Banking --- Investment & securities --- Consumer price indexes --- Central bank policy rate --- Nominal effective exchange rate --- Treasury bills and bonds --- Real effective exchange rates --- Prices --- Financial services --- Financial institutions --- Price indexes --- Interest rates --- Government securities --- United States