EconPapers    
Economics at your fingertips  
 

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 ().

 
Page updated 2025-03-22
Handle: RePEc:taf:apeclt:v:27:y:2020:i:17:p:1434-1437