Evaluating the Calibration of Multi-Step-Ahead Density Forecasts Using Raw Moments

Abstract

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 which take this problem into account. However, these approaches can be computationally burdensome, ignore important information and therefore lack power, or suffer from size distortions even asymptotically. In this work, a fourth testing approach based on raw moments is proposed. It is 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 if it is based directly on the (standardized) probability integral transforms.