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A goodness-of-fit test for Generalized Error Distribution

Author

Listed:
  • Daniele Coin

    (Bank of Italy)

Abstract

The Generalized Error Distribution is a widely used flexible family of symmetric probability distribution. Thanks to its properties, it is becoming more and more popular in many fields of science, and therefore it is important to determine whether a sample is drawn from a GED, usually done using a graphical approach. In this paper we present a new goodness-of-fit test for GED that performs well in detecting non-GED distribution when the alternative distribution is either skewed or a mixture. A comparison between well-known tests and this new procedure is performed through a simulation study. We have developed a function that performs the analysis described in this paper in the R environment. The computational time required to compute this procedure is negligible.

Suggested Citation

  • Daniele Coin, 2017. "A goodness-of-fit test for Generalized Error Distribution," Temi di discussione (Economic working papers) 1096, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:wptemi:td_1096_17
    as

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    File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2017/2017-1096/en_tema_1096.pdf
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    References listed on IDEAS

    as
    1. Andrew C. Harvey, 1990. "The Econometric Analysis of Time Series, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026208189x, December.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Marsaglia, George & Marsaglia, John, 2004. "Evaluating the Anderson-Darling Distribution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i02).
    4. J. P. Royston, 1982. "Expected Normal Order Statistics (Exact and Approximate)," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 161-165, June.
    5. Chen, Carl R. & Su, Yuli & Huang, Ying, 2008. "Hourly index return autocorrelation and conditional volatility in an EAR-GJR-GARCH model with generalized error distribution," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 789-798, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    exponential power distribution; kurtosis; normal standardized Q-Q plot;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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