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Testing Conditional Symmetry Without Smoothing

Listed author(s):
  • Tao Chen

    (University of Connecticut)

  • Gautam Tripathi

    (University of Connecticut)

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.

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Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2011-01.

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Length: 53 pages
Date of creation: Jan 2011
Handle: RePEc:uct:uconnp:2011-01
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