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Goodness-of-Fit Tests for a Multivariate Distribution by the Empirical Characteristic Function

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  • Fan, Yanqin

Abstract

In this paper, we take the characteristic function approach to goodness-of-fit tests. It has several advantages over existing methods: First, unlike the popular comparison density function approach suggested in Parzen (1979), our approach is applicable to both univariate and multivariate data; Second, in the case where the null hypothesis is composite, the approach taken in this paper yields a test that is superior to tests based on empirical distribution functions such as the Cramér- von Mises test, because on the one hand the asymptotic critical values of our test are easily obtained from the standard normal distribution and are not affected by-consistent estimation of the unknown parameters in the null hypothesis, and on the other hand, our test extends that in Eubank and LaRiccia (1992) and hence is more powerful than the Cramér-von Mises test for high-frequency alternatives.

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  • Fan, Yanqin, 1997. "Goodness-of-Fit Tests for a Multivariate Distribution by the Empirical Characteristic Function," Journal of Multivariate Analysis, Elsevier, vol. 62(1), pages 36-63, July.
    • Handle: RePEc:eee:jmvana:v:62:y:1997:i:1:p:36-63
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    References listed on IDEAS

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    1. L. Baringhaus & N. Henze, 1988. "A consistent test for multivariate normality based on the empirical characteristic function," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 35(1), pages 339-348, December.
    2. Hall, Peter, 1984. "Central limit theorem for integrated square error of multivariate nonparametric density estimators," Journal of Multivariate Analysis, Elsevier, vol. 14(1), pages 1-16, February.
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    Cited by:

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    4. M. V. Alba & D. Barrera & M. D. Jiménez, 2001. "A homogeneity test based on empirical characteristic functions," Computational Statistics, Springer, vol. 16(2), pages 255-270, July.
    5. Gouriéroux, Christian & Tenreiro, Carlos, 2001. "Local Power Properties of Kernel Based Goodness of Fit Tests," Journal of Multivariate Analysis, Elsevier, vol. 78(2), pages 161-190, August.
    6. Henze, N. & Klar, B. & Zhu, L. X., 2005. "Checking the adequacy of the multivariate semiparametric location shift model," Journal of Multivariate Analysis, Elsevier, vol. 93(2), pages 238-256, April.
    7. Amengual, Dante & Carrasco, Marine & Sentana, Enrique, 2020. "Testing distributional assumptions using a continuum of moments," Journal of Econometrics, Elsevier, vol. 218(2), pages 655-689.
    8. Scaillet, Olivier, 2007. "Kernel-based goodness-of-fit tests for copulas with fixed smoothing parameters," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 533-543, March.
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    11. Chen, Feifei & Jiménez–Gamero, M. Dolores & Meintanis, Simos & Zhu, Lixing, 2022. "A general Monte Carlo method for multivariate goodness–of–fit testing applied to elliptical families," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
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    14. Jiménez-Gamero, M.D. & Alba-Fernández, V. & Muñoz-García, J. & Chalco-Cano, Y., 2009. "Goodness-of-fit tests based on empirical characteristic functions," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 3957-3971, October.
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    17. Marie Hušková & Simos Meintanis, 2008. "Tests for the multivariate -sample problem based on the empirical characteristic function," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(3), pages 263-277.

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