Forecasting Asymmetric Unemployment Rates
比较了多种非线性时间序列模型对美国战后季度失业率的预测效果，发现部分非线性模型优于传统线性模型，但结果受数据平稳化处理影响。
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