On preliminary test and shrinkage estimation in linear models with long-memory errors

Abstract

This paper discusses the problem of estimating a subset of parameters when the complementary subset is possibly redundant, in a linear regression model when the errors are generated from a long-memory process. Such a model arises due to the overmodelling of a situation involving long-memory data. Along with the classical least-squares estimator and restricted least-squares estimator, preliminary test least-squares estimator and shrinkage least-squares estimator are investigated in an asymptotic set-up and their relative performances are studied under contiguous alternatives. The contiguous alternatives under such dependence are fundamentally different from those under the independent errors case.