Minimum Distance Lack-of-Fit Tests under Long Memory Errors

This paper discusses some tests of lack-of-fit of a parametric regression model when errors form a long memory moving average process with the long memory parameter 0 < d < 1/2, and when design is non-random and uniform on [0, 1]. These tests are based on certain minimized distances between a nonparametric regression function estimator and the parametric model being fitted. The paper investigates the asymptotic null distribution of the proposed test statistics and of the corresponding minimum distance estimators under minimal conditions on the model being fitted. The limiting distribution of these statistics are Gaussian for 0 < d < 1/4 and non-Gaussian for 1/4 < d < 1/2. We also discuss the consistency of these tests against a fixed alternative.