Working Paper, Department of Economics, Queen Mary, University of London 587
This paper considers the problem of statistical inference in linear regression models whose stochastic regressors and errors may exhibit long-range dependence. A time-domain sieve-type generalized least squares (GLS) procedure is proposed based on an autoregressive approximation to the generating mechanism of the errors. The asymptotic properties of the sieve-type GLS estimator are established. A Monte Carlo study examines the finite-sample properties of the method for testing regression hypotheses.
Autoregressive approximation, Generalized least squares, Linear regression, Long-range dependence, Spectral density