Lasso Regressions and Forecasting Models in Applied Stress Testing
35 Pages Posted: 16 Oct 2017
There are 2 versions of this paper
Lasso Regressions and Forecasting Models in Applied Stress Testing
Date Written: May 2017
Abstract
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.
Keywords: Economic forecasting, Forecasting models, Regression analysis, Stress testing, Emerging markets, Stress test, machine learning, model selection, lasso, relaxed lasso, General, Forecasting and Other Model Applications
JEL Classification: C10, C53, G21
Suggested Citation: Suggested Citation