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Testing for the generalized normal-Laplace distribution with applications

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  • Meintanis, Simos G.
  • Tsionas, Efthimios

Abstract

The generalized normal-Laplace distribution is a useful law for modelling asymmetric data exhibiting excess kurtosis. Goodness-of-fit tests for this distribution are constructed which utilize the corresponding moment generating function, and its empirical counterpart. The consistency and other properties of the test are investigated under general assumptions, and the proposed procedure is applied, following a non-trivial estimation step, to test the fit of some financial data.

Suggested Citation

  • Meintanis, Simos G. & Tsionas, Efthimios, 2010. "Testing for the generalized normal-Laplace distribution with applications," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3174-3180, December.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:12:p:3174-3180
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    References listed on IDEAS

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    1. Lau, Amy Hing-Ling & Lau, Hon-Shiang & Wingender, John R, 1990. "The Distribution of Stock Returns: New Evidence against the Stable Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 217-223, April.
    2. Huang, Da & Wang, Hansheng & Yao, Qiwei, 2008. "Estimating GARCH models: when to use what?," LSE Research Online Documents on Economics 5398, London School of Economics and Political Science, LSE Library.
    3. Tenreiro, Carlos, 2009. "On the choice of the smoothing parameter for the BHEP goodness-of-fit test," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1038-1053, February.
    4. Tucker, Alan L, 1992. "A Reexamination of Finite- and Infinite-Variance Distributions as Models of Daily Stock Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(1), pages 73-81, January.
    5. Madan, Dilip B & Seneta, Eugene, 1990. "The Variance Gamma (V.G.) Model for Share Market Returns," The Journal of Business, University of Chicago Press, vol. 63(4), pages 511-524, October.
    6. Norbert Henze & Simos G. Meintanis, 2005. "Recent and classical tests for exponentiality: a partial review with comparisons," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 61(1), pages 29-45, February.
    7. Da Huang & Hansheng Wang & Qiwei Yao, 2008. "Estimating GARCH models: when to use what?," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 27-38, March.
    8. Meintanis, Simos G., 2008. "A new approach of goodness-of-fit testing for exponentiated laws applied to the generalized Rayleigh distribution," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2496-2503, January.
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    Cited by:

    1. Eduardo Gutiérrez González & José Villaseñor Alva & Olga Panteleeva & Humberto Vaquera Huerta, 2013. "On testing the log-gamma distribution hypothesis by bootstrap," Computational Statistics, Springer, vol. 28(6), pages 2761-2776, December.
    2. L. Baringhaus & B. Ebner & N. Henze, 2017. "The limit distribution of weighted $$L^2$$ L 2 -goodness-of-fit statistics under fixed alternatives, with applications," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(5), pages 969-995, October.

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