This paper presents a comparison of prediction performances of three kernel-based nonparametric methods applied to the U.S. weekly T-bill rate. Predictions are generated through the rolling approach for the out-of-sample period 1989-1993. We compare the multistep-ahead prediction performance of the conditional mean, the conditional median, and the conditional mode with the performance of the benchmark random walk model. Using four prediction evaluation criteria, it is shown that two of the three predictors are superior -- or at least equal -- to the random walk at prediction horizons 1 - 5. In addition, by combining two of the three predictors, a significant improvement in prediction accuracy is obtained at all prediction horizons. Also the combined predictions result in substantial improvement at predicting the direction of change. Further, we propose two prediction intervals based on the estimated nonparametric conditional distribution function. These intervals are useful when the predictive distribution underlying the time series process is asymmetric or multi-modal. Finally, we assess the choice of the bandwidth in the kernel-based prediction methods through a recently proposed method for evaluating the estimated prediction densities.
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