Lasso Regressions and Forecasting Models in Applied Stress Testing

Jorge Chan-Lau

No 2017/108, IMF Working Papers from International Monetary Fund

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: WP; Lasso regression; Lasso method; estimation framework; Stress test; forecasting; machine learning; model selection; lasso; relaxed lasso; money market rate; U.S. dollar; Consumer price indexes; Central bank policy rate; Nominal effective exchange rate; Treasury bills and bonds; Real effective exchange rates; Global (search for similar items in EconPapers)
Pages: 34
Date: 2017-05-05
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.imf.org/external/pubs/cat/longres.aspx?sk=44887 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:imf:imfwpa:2017/108

Ordering information: This working paper can be ordered from
http://www.imf.org/external/pubs/pubs/ord_info.htm

Access Statistics for this paper

More papers in IMF Working Papers from International Monetary Fund International Monetary Fund, Washington, DC USA. Contact information at EDIRC.
Bibliographic data for series maintained by Akshay Modi ().