Semiparametric Sieve-Type Generalized Least Squares Inference

George Kapetanios and Zacharias Psaradakis

Econometric Reviews, 2016, vol. 35, issue 6, 951-985

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.

Date: 2016
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DOI: 10.1080/07474938.2014.975639

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