A test of the conditional independence assumption in sample selection models
Journal
Journal of Applied Econometrics
ISSN
0883-7252
Type
journal article
Date Issued
2014-09-10
Author(s)
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.
Language
English
Keywords
sample selection
quantile regression
independence
test
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Wiley-Blackwell
Publisher place
Chichester
Number
forthcoming
Start page
NA
Subject(s)
Division(s)
Eprints ID
80559
File(s)![Thumbnail Image]()
open.access
Name
selection test.pdf
Size
258.09 KB
Format
Adobe PDF
Checksum (MD5)
a2d1d77408b124d051ddd187c69682ab