Optimal Bandwidth Selection for Forecasting under Parameter Instability
针对预测模型中参数不稳定的问题，提出一种通过最小化条件期望损失来选择带宽的方法，理论证明其渐近最优性，蒙特卡洛模拟和实证应用（预测超额债券收益率和收益率曲线）显示其优越的预测性能。
Abstract This article addresses practical issues associated with the use of the local estimator in forecasting models that are affected by parameter instability. We propose an approach to select the bandwidth parameter in the context of out-of-sample forecasting. Derived by minimizing the conditional expected end-of-sample loss, the selection procedure is shown to be asymptotically optimal. We also discuss the implications of the choice of kernel functions. The theoretical properties are examined through an extensive Monte Carlo study. Two empirical applications on forecasting excess bond returns and the yield curve demonstrate the superior forecasting performance of the local estimator with the proposed optimal bandwidth selection.