Forecasting Asymmetric Unemployment Rates
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
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