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    Using the Bootstrap to test for symmetry under unknown dependence

    Psaradakis, Zacharias (2016) Using the Bootstrap to test for symmetry under unknown dependence. Journal of Business and Economic Statistics 34 (3), pp. 406-415. ISSN 0735-0015.

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    Abstract

    This paper considers tests for symmetry of the one-dimensional marginal distribution of fractionally integrated processes. The tests are implemented by using an autoregressive sieve bootstrap approximation to the null sampling distribution of the relevant test statistics. The sieve bootstrap allows inference on symmetry to be carried out without knowledge of either the memory parameter of the data or of the appropriate norming factor for the test statistic and its asymptotic distribution. The small-sample properties of the proposed method are examined by means of Monte Carlo experiments, and applications to real-world data are also presented.

    Metadata

    Item Type: Article
    Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis, available online: http://wwww.tandfonline.com/10.1080/07350015.2015.1043368
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Zacharias Psaradakis
    Date Deposited: 27 Nov 2015 13:45
    Last Modified: 03 Apr 2017 01:05
    URI: http://eprints.bbk.ac.uk/id/eprint/12370

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