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Testing for normality with applications

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  • Marian Vavra

    (National Bank of Slovakia, Research Department)

Abstract

This paper considers the problem of testing for normality of the marginal law of univariate and multivariate stationary and weakly dependent random processes using a bootstrap-based Anderson-Darling test statistic. The finite-sample properties of the test are assessed via Monte Carlo experiments. An application to the inflation forecast errors is also presented.

Suggested Citation

  • Marian Vavra, 2015. "Testing for normality with applications," Working and Discussion Papers WP 1/2015, Research Department, National Bank of Slovakia.
    • Handle: RePEc:svk:wpaper:1031
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Marián Vávra, 2020. "Assessing distributional properties of forecast errors for fan-chart modelling," Empirical Economics, Springer, vol. 59(6), pages 2841-2858, December.

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    More about this item

    Keywords

    testing for normality; Anderson-Darling statistic; sieve bootstrap; 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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