BIROn - Birkbeck Institutional Research Online

    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.

    26861.pdf - Draft Version

    Download (447kB) | Preview


    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.


    Item Type: Monograph (Working Paper)
    Additional Information: BWPEF 1706
    Keyword(s) / Subject(s): Autoregressive sieve bootstrap, Normality test, Weak dependence
    School: Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School
    Depositing User: Administrator
    Date Deposited: 25 Mar 2019 13:41
    Last Modified: 02 Aug 2023 17:49


    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