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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)
Date: 2002
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Related works:
Journal Article: Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method (2004) Downloads
Working Paper: Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method (2002) Downloads
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