Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/62917
Authors: 
Kapetanios, George
Psaradakis, Zacharias
Year of Publication: 
2007
Series/Report no.: 
Working Paper, Department of Economics, Queen Mary, University of London 587
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.
Subjects: 
Autoregressive approximation, Generalized least squares, Linear regression, Long-range dependence, Spectral density
JEL: 
C12
C13
C22
Document Type: 
Working Paper

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