Semiparametric Sieve-Type Generalized Least Squares Inference |
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| Authors: | George Kapetanios Zacharias Psaradakis |
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| Institution: | 1. School of Economics and Finance, Queen Mary, University of London, London, U.K.;2. Department of Economics, Mathematics and Statistics, Birkbeck, University of London, London, U.K. |
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| Abstract: | This article considers the problem of statistical inference in linear regression models with dependent errors. A 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 under general conditions, including mixingale-type conditions as well as conditions which allow for long-range dependence in the stochastic regressors and/or the errors. A Monte Carlo study examines the finite-sample properties of the method for testing regression hypotheses. |
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| Keywords: | Autoregressive approximation Generalized least squares Linear regression Long-range dependence Short-range dependence |
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