BIROn - Birkbeck Institutional Research Online

    Semiparametric sieve-type generalized least squares inference

    Kapetanios, G. and Psaradakis, Zacharias (2014) Semiparametric sieve-type generalized least squares inference. Econometric Reviews 35 (6), pp. 951-985. ISSN 0747-4938.

    ER_2015.pdf - Author's Accepted Manuscript

    Download (812kB) | Preview


    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.


    Item Type: Article
    Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis, available online:
    Keyword(s) / Subject(s): Autoregressive approximation, Generalized least squares, Linear regression, Long-range dependence, Short-range dependence, C22
    School: Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School
    Depositing User: Zacharias Psaradakis
    Date Deposited: 19 Jun 2015 11:26
    Last Modified: 02 Aug 2023 17:17


    Activity Overview
    6 month trend
    6 month trend

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item