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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.

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File URL: http://www.econ.uconn.edu/working/2011-01.pdf
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Bibliographic Info

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
Date of revision:
Handle: RePEc:uct:uconnp:2011-01

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Cited by:
  1. Tao Chen & Gautam Tripathi, 2014. "A simple consistent test of conditional symmetry in symmetrically trimmed tobit models," CREA Discussion Paper Series 14-04, Center for Research in Economic Analysis, University of Luxembourg.

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