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

    Psaradakis, Zacharias and Vávra, M. (2018) Normality tests for dependent data: large-sample and bootstrap approaches. Communications in Statistics - Simulation and Computation , ISSN 0361-0918. (In Press)

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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: Article
    Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis, available online at the link above.
    School: School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Zacharias Psaradakis
    Date Deposited: 31 May 2018 15:14
    Last Modified: 11 Feb 2021 09:20
    URI: https://eprints.bbk.ac.uk/id/eprint/22612

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