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Testing conditional symmetry without smoothing

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  • Tao Chen
  • Gautam Tripathi

Abstract

We test the assumption of conditional symmetry used to identify and estimate parameters in regression models with endogenous regressors, without making any distributional assumptions. The Kolmogorov-Smirnov-type statistic we propose is consistent, computationally tractable because it does not require optimisation over an uncountable set, free of any kind of nonparametric smoothing, and can detect n -super-1/2-deviations from the null. Results from a simulation experiment suggest that our test can work very well in moderately sized samples.

Suggested Citation

  • Tao Chen & Gautam Tripathi, 2013. "Testing conditional symmetry without smoothing," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(2), pages 273-313, June.
    • Handle: RePEc:taf:gnstxx:v:25:y:2013:i:2:p:273-313
    DOI: 10.1080/10485252.2012.752083
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    References listed on IDEAS

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    Cited by:

    1. Masayuki Hirukawa & Mari Sakudo, 2016. "Testing Symmetry of Unknown Densities via Smoothing with the Generalized Gamma Kernels," Econometrics, MDPI, vol. 4(2), pages 1-27, June.
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    3. Niu, Cuizhen & Guo, Xu & Li, Yong & Zhu, Lixing, 2018. "Pairwise distance-based tests for conditional symmetry," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 145-162.

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    More about this item

    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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