Testing Normality for Linear AR( p ) Models

Abstract

This paper proposes a nonparametric mixed test for normality of linear autoregressive time series. The test is based on the best one-step forecast in mean square with time reverse. The test statistic is the mixture of a goodness of fit statistic and Cramer–Von Mises statistic. Some asymptotic properties are developed for the test. Simulated results have shown that the test is easy to use and has good powers. Three examples of applying the test to real data are also included.