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    Normality tests for dependent data: large-sample and bootstrap approaches

    Psaradakis, Zacharias and Vavra, Marian (2017) Normality tests for dependent data: large-sample and bootstrap approaches. Working Paper. Birkbeck, University of London, London, UK.

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    Abstract

    The paper considers the problem of testing for normality of the one-dimensional marginal distribution of a strictly stationary and weakly dependent stochastic process. The possibility of using an autoregressive sieve bootstrap procedure to obtain critical values and P-values for normality tests is explored. The small-sample properties of a variety of tests are investigated in an extensive set of Monte Carlo experiments. The bootstrap version of the classical skewness-kurtosis test is shown to have the best overall performance in small samples.

    Metadata

    Item Type: Monograph (Working Paper)
    Additional Information: BWPEF 1706
    Keyword(s) / Subject(s): Autoregressive sieve bootstrap, Normality test, Weak dependence
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Administrator
    Date Deposited: 25 Mar 2019 13:41
    Last Modified: 28 Jul 2019 06:09
    URI: http://eprints.bbk.ac.uk/id/eprint/26861

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