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

提案由两个项目组成。第一个项目提出了在损失函数是非对称时估计和评估预测模型的方法的发展。目前使用的大多数估计方法对二次损失函数是最优的，但对其他方法则不是。特别是，非对称损失函数通常需要使用偏估计量。第二个项目将频域方法扩展到参数模型的估计。P.I.建议使用自举程序来寻找估计光谱的置信通道。

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.