A Test of the Conditional Independence Assumption in Sample Selection Models

Identification in most sample selection models depends on the independence of the regressors and the error terms conditional on the selection probability. All quantile and mean functions are parallel in these models; this implies that quantile estimators cannot reveal any—per assumption non-existing...

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Bibliographic Details
Published in:Journal of applied econometrics (Chichester, England) England), 2015-11, Vol.30 (7), p.1144-1168
Main Authors: Huber, Martin, Melly, Blaise
Format: Article
Language:English
Subjects:
Decision making models
Estimating techniques
Mathematical functions
Measurement errors
Monte Carlo simulation
Regression analysis
Studies
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Summary:Identification in most sample selection models depends on the independence of the regressors and the error terms conditional on the selection probability. All quantile and mean functions are parallel in these models; this implies that quantile estimators cannot reveal any—per assumption non-existing—heterogeneity. Quantile estimators are nevertheless useful for testing the conditional independence assumption because they are consistent under the null hypothesis. We propose tests of the Kolmogorov–Smirnov type based on the conditional quantile regression process. Monte Carlo simulations show that their size is satisfactory and their power sufficient to detect deviations under plausible data-generating processes. We apply our procedures to female wage data from the 2011 Current Population Survey and show that homogeneity is clearly rejected.
ISSN:0883-7252
1099-1255
DOI:10.1002/jae.2431