George Kapetanios () (Queen Mary, University of London) Zacharias Psaradakis () (Birkbeck, University of London)
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.
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Publisher Info
Paper provided by Queen Mary, University of London, Department of Economics in its series Working Papers with number
587.