A General Statistic for Testing the Validity of a Model’s Forecasts

Abstract

The paper introduces a new approach for testing the validity of a model's forecasts and it is established that the proposed test is exact for location-scale family. The results are used to introduce goodness-of-fit tests for the normal, exponential, uniform and Laplace distributions. Through Monte Carlo simulation, the power values of the proposed test under various alternatives are compared with the other tests. The proposed test has higher power than the competing tests against some alternatives. The use of the proposed test is shown in real examples.