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Lasso Regressions and Forecasting Models in Applied Stress Testing.

Author: Jorge A Chan-Lau
Publisher: Washington, D.C. : International Monetary Fund, 2017.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Chan-Lau, Jorge A.
Lasso Regressions and Forecasting Models in Applied Stress Testing.
Washington, D.C. : International Monetary Fund, ©2017
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jorge A Chan-Lau
ISBN: 9781475599329 1475599323
OCLC Number: 987699985
Description: 1 online resource (35 pages)

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.

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