Publication: Conditional stochastic dominance testing
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2011-12
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Abstract
This article proposes bootstrap-based stochastic dominance tests for nonparametric
conditional distributions and their moments. We exploit the fact
that a conditional distribution dominates the other if and only if the
difference between the marginal joint distributions is monotonic in the
explanatory variable for each value of the dependent variable. The proposed
test statistic compares restricted and unrestricted estimators of the difference
between the joint distributions, and can be implemented under minimal
smoothness requirements on the underlying nonparametric curves and without
resorting to smooth estimation. The finite sample properties of the proposed
tests are examined by means of a Monte Carlo study. We report an application
to studying the impact on post-intervention earnings of the National Supported
Work Demonstration, a randomized labor training program carried out in the
1970s.
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Nonparametric testing, Conditional stochastic dominance, Conditional inequality restrictions, Least concave majorant, Treatment effects