No. 587: Semiparametric Sieve-Type GLS Inference in Regressions with Long-Range Dependence

George Kapetanios Queen Mary, University of London Zacharias Psaradakis Birkbeck, University of London

March 1, 2007

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Abstract

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.

J.E.L classification codes: C12, C13, C22

Keywords: Autoregressive approximation, Generalized least squares, Linear regression, Long-range dependence, Spectral density