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A distance test of normality for a wide class of stationary processes

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  • Psaradakis, Zacharias
  • Vávra, Marián

Abstract

A distance test for normality of the one-dimensional marginal distribution of stationary fractionally integrated processes is considered. The test is implemented by using an autoregressive sieve bootstrap approximation to the null sampling distribution of the test statistic. The bootstrap-based test does not require knowledge of either the dependence parameter of the data or of the appropriate norming factor for the test statistic. The small-sample properties of the test are examined by means of Monte Carlo experiments. An application to real-world data is also presented.

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  • Psaradakis, Zacharias & Vávra, Marián, 2017. "A distance test of normality for a wide class of stationary processes," Econometrics and Statistics, Elsevier, vol. 2(C), pages 50-60.
    • Handle: RePEc:eee:ecosta:v:2:y:2017:i:c:p:50-60
    DOI: 10.1016/j.ecosta.2016.11.005
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    Cited by:

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    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

    Distance test; Fractionally integrated process; Sieve bootstrap; Normality;
    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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