EconPapers    
Economics at your fingertips  
 

Bottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults using Machine Learning

Tyler Pike, Horacio Sapriza and Tom Zimmermann

No 2019-070, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: This paper constructs a leading macroeconomic indicator from microeconomic data using recent machine learning techniques. Using tree-based methods, we estimate probabilities of default for publicly traded non-financial firms in the United States. We then use the cross-section of out-of-sample predicted default probabilities to construct a leading indicator of non-financial corporate health. The index predicts real economic outcomes such as GDP growth and employment up to eight quarters ahead. Impulse responses validate the interpretation of the index as a measure of financial stress.

Keywords: Corporate Default; Early Warning Indicators; Economic Activity; Machine Learning (search for similar items in EconPapers)
JEL-codes: C53 E32 G33 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2019-09-20
New Economics Papers: this item is included in nep-big, nep-cmp, nep-mac and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed

Downloads: (external link)
https://www.federalreserve.gov/econres/feds/files/2019070pap.pdf (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:fip:fedgfe:2019-70

DOI: 10.17016/FEDS.2019.070

Access Statistics for this paper

More papers in Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.) Contact information at EDIRC.
Bibliographic data for series maintained by Ryan Wolfslayer ; Keisha Fournillier ().

 
Page updated 2022-09-27
Handle: RePEc:fip:fedgfe:2019-70