Lasso Regressions and Forecasting Models in Applied Stress Testing

May 5, 2017

Preview Citation

Format: Chicago

Jorge A Chan-Lau. Lasso Regressions and Forecasting Models in Applied Stress Testing, (USA: International Monetary Fund, 2017) accessed January 23, 2025

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

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.

Subject: Central bank policy rate, Consumer price indexes, Financial institutions, Financial services, Foreign exchange, Nominal effective exchange rate, Prices, Real effective exchange rates, Treasury bills and bonds

Keywords: Central bank policy rate, Consumer price indexes, Estimation framework, Forecasting, Global, Lasso, Lasso method, Lasso regression, Machine learning, Model selection, Money market rate, Nominal effective exchange rate, Real effective exchange rates, Relaxed lasso, Stress test, Treasury bills and bonds, U.S. dollar, WP

Publication Details

Pages:

34
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2017/108
Stock No:

WPIEA2017108
ISBN:

9781475599022
ISSN:

1018-5941

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