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A Trinomial Test for Paired Data When There are Many Ties

Author

Listed:
  • Guorui Bian

    (Department of Statistics, East China Normal University)

  • Michael McAleer

    (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University)

  • Wing-Keung Wong

    (Department of Economics, Hong Kong Baptist University)

Abstract

This paper develops a new test, the trinomial test, for pairwise ordinal data samples to improve the power of the sign test by modifying its treatment of zero differences between observations, thereby increasing the use of sample information. Simulations demonstrate the power superiority of the proposed trinomial test statis- tic over the sign test in small samples in the presence of tie observations. We also show that the proposed trinomial test has substantially higher power than the sign test in large samples and also in the presence of tie observations, as the sign test ignores information from observations resulting in ties.

Suggested Citation

  • Guorui Bian & Michael McAleer & Wing-Keung Wong, 2010. "A Trinomial Test for Paired Data When There are Many Ties," KIER Working Papers 736, Kyoto University, Institute of Economic Research.
  • Handle: RePEc:kyo:wpaper:736
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    File URL: http://www.kier.kyoto-u.ac.jp/DP/DP736.pdf
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    References listed on IDEAS

    as
    1. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-430, March.
    2. Hansen, Bruce E., 2000. "Testing for structural change in conditional models," Journal of Econometrics, Elsevier, vol. 97(1), pages 93-115, July.
    3. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, pages 1383-1414.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Sign test; trinomial test; non-parametric test; ties; test statistics; hypothesis testing.;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • 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

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