Semiparametric Sieve-Type GLS Inference in Regressions with Long-Range Dependence

George Kapetanios and Zacharias Psaradakis

No 587, Working Papers from Queen Mary University of London, School of Economics and Finance

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.

Keywords: Autoregressive approximation; Generalized least squares; Linear regression; Long-range dependence; Spectral density (search for similar items in EconPapers)
JEL-codes: C12 C13 C22 (search for similar items in EconPapers)
Date: 2007-03-01
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Persistent link: https://EconPapers.repec.org/RePEc:qmw:qmwecw:587

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