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Normality Tests for Dependent Data: Large-Sample and Bootstrap Approaches

Author

Listed:
  • Zacharias Psaradakis

    (Birkbeck, University of London)

  • Marián Vávra

    (National Bank of Slovakia)

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.

Suggested Citation

  • Zacharias Psaradakis & Marián Vávra, 2017. "Normality Tests for Dependent Data: Large-Sample and Bootstrap Approaches," Birkbeck Working Papers in Economics and Finance 1706, Birkbeck, Department of Economics, Mathematics & Statistics.
    • Handle: RePEc:bbk:bbkefp:1706
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    File URL: https://eprints.bbk.ac.uk/id/eprint/26861
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    References listed on IDEAS

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    Cited by:

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    3. Bogdan Włodarczyk & Daniela Firoiu & George H. Ionescu & Florin Ghiocel & Marek Szturo & Lesław Markowski, 2021. "Assessing the Sustainable Development and Renewable Energy Sources Relationship in EU Countries," Energies, MDPI, vol. 14(8), pages 1-16, April.

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    More about this item

    Keywords

    Autoregressive sieve bootstrap; Normality test; Weak dependence.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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