Can machine learning on economic data better forecast the unemployment rate?
Aaron Kreiner and
John Duca
Applied Economics Letters, 2020, vol. 27, issue 17, 1434-1437
Abstract:
Using FRED data, a machine-learning model outperforms the Survey of Professional Forecasters and other models since 2001 in forecasting the unemployment rate.
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://hdl.handle.net/10.1080/13504851.2019.1688237 (text/html)
Access to full text is restricted to subscribers.
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:taf:apeclt:v:27:y:2020:i:17:p:1434-1437
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEL20
DOI: 10.1080/13504851.2019.1688237
Access Statistics for this article
Applied Economics Letters is currently edited by Anita Phillips
More articles in Applied Economics Letters from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().