Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method

Amos Golan and Jeffrey Perloff

No 25060, CUDARE Working Papers from University of California, Berkeley, Department of Agricultural and Resource Economics

Abstract: We use a nonlinear, nonparametric method to forecast the unemployment rates. We compare these forecasts to several linear and nonlinear parametric methods based on the work of Montgomery et al. (1998) and Carruth et al. (1998). Our main result is that, due to the nonlinearity in the data generating process, the nonparametric method outperforms many other wellknown models, even when these models use more information. This result holds for forecasts based on quarterly and on monthly data.

Keywords: Labor; and; Human; Capital (search for similar items in EconPapers)
Pages: 24
Date: 2002
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/25060/files/wp020956.pdf (application/pdf)

Related works:
Working Paper: Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method (2002)
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:ags:ucbecw:25060

DOI: 10.22004/ag.econ.25060

Access Statistics for this paper

More papers in CUDARE Working Papers from University of California, Berkeley, Department of Agricultural and Resource Economics Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().