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Nonparametric tests for tail monotonicity

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  • Berghaus, Betina
  • Bücher, Axel

Abstract

This article proposes nonparametric tests for tail monotonicity of bivariate random vectors. The test statistic is based on a Kolmogorov–Smirnov-type functional of the empirical copula. Depending on the serial dependence features of the data, we propose two multiplier bootstrap techniques to approximate the critical values. We show that the test is able to detect local alternatives converging to the null hypothesis at rate n−1/2 with a non-trivial power. A simulation study is performed to investigate the finite-sample performance and finally the procedure is illustrated by testing intergenerational income mobility and testing a market data set.

Suggested Citation

  • Berghaus, Betina & Bücher, Axel, 2014. "Nonparametric tests for tail monotonicity," Journal of Econometrics, Elsevier, vol. 180(2), pages 117-126.
    • Handle: RePEc:eee:econom:v:180:y:2014:i:2:p:117-126
    DOI: 10.1016/j.jeconom.2014.03.005
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    1. Du, Xiaodong & Hennessy, David & Feng, Hongli, 2014. "Tail Dependence is to be Expected Among Crop Yields," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 174315, Agricultural and Applied Economics Association.

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    More about this item

    Keywords

    Copula; Left tail decreasing; Multiplier bootstrap; Ranks; Tail monotonicity;
    All these keywords.

    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

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