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

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.


Issue Date:
2002
Publication Type:
Working or Discussion Paper
PURL Identifier:
http://purl.umn.edu/25060
Total Pages:
24
Series Statement:
CUDARE Working Paper 956




 Record created 2017-04-01, last modified 2017-04-04

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