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Test of misspecification with application to negative binomial distribution

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  • K. Chua
  • S. Ong

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    Abstract

    A misspecification test based directly on Bartlett’s First Identity is examined. This test is exemplified by the negative binomial distribution. A Monte Carlo simulation study has been conducted, in the context of testing distributional misspecification, and the performance of the proposed test has been benchmarked with some goodness-of-fit tests based on the empirical distribution function. The results suggest that the proposed test is viable in terms of computational speed and statistical power, and has the advantage that complications arising from the use of the covariance matrix in White’s information matrix test are avoided. Copyright Springer-Verlag 2013

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    File URL: http://hdl.handle.net/10.1007/s00180-012-0345-x
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    Bibliographic Info

    Article provided by Springer in its journal Computational Statistics.

    Volume (Year): 28 (2013)
    Issue (Month): 3 (June)
    Pages: 993-1009

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    Handle: RePEc:spr:compst:v:28:y:2013:i:3:p:993-1009

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    Web page: http://www.springerlink.com/link.asp?id=120306

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    Related research

    Keywords: Bartlett’s First Identity; Information matrix test; Empirical distribution tests; Discrete distributions; Orthogonal parameters; Monte Carlo simulation; Bootstrap; Biased-corrected accelerated;

    References

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    1. Chesher, Andrew & Spady, Richard, 1991. "Asymptotic Expansions of the Information Matrix Test Statistic," Econometrica, Econometric Society, vol. 59(3), pages 787-815, May.
    2. Taylor, Larry W., 1987. "The size bias of White's information matrix test," Economics Letters, Elsevier, vol. 24(1), pages 63-67.
    3. Gupta, Ramesh C. & Ong, S. H., 2004. "A new generalization of the negative binomial distribution," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 287-300, March.
    4. Horowitz, Joel L., 1994. "Bootstrap-based critical values for the information matrix test," Journal of Econometrics, Elsevier, vol. 61(2), pages 395-411, April.
    5. Ong, S.H. & Lee, Wen-Jau, 2008. "Computer generation of negative binomial variates by envelope rejection," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4175-4183, May.
    6. Famoye, Felix, 2000. "Goodness-of-fit tests for generalized logarithmic series distribution," Computational Statistics & Data Analysis, Elsevier, vol. 33(1), pages 59-67, March.
    7. Orme, Chris, 1990. "The small-sample performance of the information-matrix test," Journal of Econometrics, Elsevier, vol. 46(3), pages 309-331, December.
    8. Goffe William L., 1996. "SIMANN: A Global Optimization Algorithm using Simulated Annealing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 1(3), pages 1-9, October.
    9. Geert Dhaene & Dirk Hoorelbeke, 2002. "The Information Matrix Test with Bootstrap-Based Covariance Matrix Estimation," Center for Economic Studies - Discussion papers ces0211, Katholieke Universiteit Leuven, Centrum voor Economische Studiën.
    10. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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