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Asymmetric behavior has been documented in postwar quarterly U.S. unemployment rates. This suggests that improvement over conventional linear forecasts may be possible through use of nonlinear time series models. In this paper an out-of-sample forecasting competition is carried out for a set of leading nonlinear time series models. It is shown that several nonlinear forecasts do indeed dominate the linear forecast. The results are sensitive, however, to whether a stationarity-inducingdoi:10.1162/003465398557276 fatcat:cvtsrp443bc2lcac3lmjoc27z4