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    Semiparametric sieve-type generalized least squares inference

    Kapetanios, G. and Psaradakis, Zacharias (2016) Semiparametric sieve-type generalized least squares inference. Econometric Reviews 35 (6), pp. 951-985. ISSN 0747-4938.

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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.

    Metadata

    Item Type: Article
    Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis, available online: http://wwww.tandfonline.com/10.1080/07474938.2014.975639
    Keyword(s) / Subject(s): Autoregressive approximation, Generalized least squares, Linear regression, Long-range dependence, Short-range dependence, C22
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Zacharias Psaradakis
    Date Deposited: 19 Jun 2015 11:26
    Last Modified: 28 Jul 2019 05:00
    URI: http://eprints.bbk.ac.uk/id/eprint/12369

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