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A test of the conditional independence assumption in sample selection models

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 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.
   
type journal paper
   
keywords sample selection, quantile regression, independence, test
   
language English
kind of paper journal article
date of appearance 10-9-2014
journal Journal of Applied Econometrics
publisher Wiley-Blackwell (Chichester)
ISSN 0883-7252
number of issue forthcoming
page(s) NA
review double-blind review
   
citation Huber, M., & Melly, B. (2014). A test of the conditional independence assumption in sample selection models. Journal of Applied Econometrics(forthcoming), NA.