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

Amos Golan and Jeffrey Perloff

Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley

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 nonlin-earity in the data generating process, the nonparametric method outperforms many other well-known models, even when these models use more information. This result holds for forecasts based on quarterly and on monthly data.

Keywords: embedding dimension; nonlinearity; nonparametric; unemployment rate (search for similar items in EconPapers)
Date: 2002-01-01
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Citations: View citations in EconPapers (3)

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Working Paper: Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method (2002)
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