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170520s2017 dcu o 000 0 eng d |
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|a Chan-Lau, Jorge A.,
|e author.
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|a Lasso Regressions and Forecasting Models in Applied Stress Testing /
|c prepared by Jorge A. Chan-Lau.
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|a [Washington, D.C.] :
|b International Monetary Fund,
|c [2017]
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|a 1 online resource (35 pages)
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|a text
|b txt
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|a IMF Working Papers,
|x 1018-5941 ;
|v WP/17/108
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|a Print version record.
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|a 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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|a eBooks on EBSCOhost
|b EBSCO eBook Subscription Academic Collection - Worldwide
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|a Recessions.
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|a Lasso.
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|a Récessions.
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|a Lasso.
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|a BUSINESS & ECONOMICS
|x Industries
|x General.
|2 bisacsh
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|a Lasso.
|2 fast
|0 (OCoLC)fst00992930
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|a Recessions.
|2 fast
|0 (OCoLC)fst01091358
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|i Print version:
|a Chan-Lau, Jorge A.
|t Lasso Regressions and Forecasting Models in Applied Stress Testing.
|d Washington, D.C. : International Monetary Fund, ©2017
|z 9781475599022
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830 |
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|a IMF working paper ;
|v WP/17/108.
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856 |
4 |
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|u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1519175
|z Texto completo
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|6 505-00/(S
|a Cover; Contents; Introduction; Stress Tests: A Multi-Step Process; Model Selection Challenges in Stress Tests; Machine Learning: The Interpretability-Flexibility Tradeoff; Linear Models: Subset Selection and Shrinkage Methods; Lasso Applications in Finance, Economics, and Financial Networks; A Stress Test Application: Forecasting Probabilities of Default; Conclusions; References; Figures; 1. Geometry of Least Squares, Ridge Regression and Lasso regression; 2. Lasso and relaxed Lasso, mean squared errors; Tables; 1. Lasso and relaxed lasso, coefficient estimates, λ-min specification.
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|6 505-00/(S
|a 2. Lasso and relaxed lasso, coefficient estimates, λ-1se specification.
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