Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/102613
Authors: 
Huber, Martin
Melly, Blaise
Year of Publication: 
2012
Series/Report no.: 
Working Paper, Brown University, Department of Economics 2012-11
Abstract: 
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 realistic data generating processes. We apply our procedures to female wage data from the 2011 Current Population Survey and show that homogeneity is clearly rejected.
Subjects: 
sample selection
quantile regression
independence
test
JEL: 
C12
C13
C14
C21
Document Type: 
Working Paper

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