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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 the use of nonlinear time-series models. In this note 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-inducing transformation is applied to the nonstationary unemployment rate series. © 1998 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology