Forecasts and Forecasting Models: Prediction, Evaluation, Estimation, and Selection Using the Relevant Loss Function

弗朗西斯迪堡宾夕法尼亚大学SBR-9520966 该提案包括两个项目。第一个项目提出了损失函数不对称时预测模型的估计和评价方法。目前使用的大多数估计方法对于二次损失函数是最优的，但对于其他损失函数不是最优的。特别是，非对称损失函数通常需要使用有偏估计。第二个项目将频域方法扩展到参数模型的估计。私家侦探建议使用引导程序，以找到估计光谱的置信隧道。

Francis Diebold University of Pennsylvania SBR-9520966 The proposal consists of two projects. The first project proposes the development of methods for estimating and evaluating forecasting models when the loss function is asymmetrical. Most estimation methods in current use are optimal for a quadratic loss function, but not for others. In particular, asymmetric loss functions will generally call for the use of biased estimators. The second project extends frequency domain methods to the estimation of parametric models. The P.I. proposes using bootstrap procedures to find confidence tunnels for estimated spectra.