Evaluating the Calibration of Multi-Step-Ahead Density Forecasts Using Raw Moments
提出一种基于原始矩的新检验方法，用于评估多步超前密度预测的校准性，该方法易于实施、使用标准临界值、可包含所有重要矩，且渐近尺寸正确，有限样本下具有良好的尺寸和功效性质。
The evaluation of multi-step-ahead density forecasts is complicated by the serial correlation of the corresponding probability integral transforms. In the literature, three testing approaches can be found that take this problem into account. However, these approaches rely on data-dependent critical values, ignore important information and, therefore lack power, or suffer from size distortions even asymptotically. This article proposes a new testing approach based on raw moments. It is extremely easy to implement, uses standard critical values, can include all moments regarded as important, and has correct asymptotic size. It is found to have good size and power properties in finite samples if it is based on the (standardized) probability integral transforms.