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Testing Conditional Symmetry Without Smoothing

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  • Tao Chen

    (University of Connecticut)

  • Gautam Tripathi

    (University of Connecticut)

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 specification test proposed here is computationally tractable, does not require nonparametric smoothing, and can detect n1/2-deviations from the null. Since the limiting distribution of the test statistic turns out to be a non-pivotal gaussian process, the critical values for implementing the test are obtained by simulation. In a Monte Carlo study we use the approach proposed here to test the assumption of conditional symmetry maintained in the seminal paper of Powell (1986b). Results from this finite sample experiment suggest that our test can work very well in moderately sized samples.

Suggested Citation

  • Tao Chen & Gautam Tripathi, 2011. "Testing Conditional Symmetry Without Smoothing," Working papers 2011-01, University of Connecticut, Department of Economics.
    • Handle: RePEc:uct:uconnp:2011-01
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    References listed on IDEAS

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    1. Hyndman, R.J. & Yao, Q., 1998. "Nonparametric Estimation and Symmetry Tests for Conditional Density Functions," Monash Econometrics and Business Statistics Working Papers 17/98, Monash University, Department of Econometrics and Business Statistics.
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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.
    2. 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.
    3. Chen, Tao & Tripathi, Gautam, 2017. "A simple consistent test of conditional symmetry in symmetrically trimmed tobit models," Journal of Econometrics, Elsevier, vol. 198(1), pages 29-40.

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    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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