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Testing for quantile sample selection
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DOI：10.1093/ectj/utac027.png)
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Abstract
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This paper provides distribution free tests for detecting sample selection in conditional quantile functions. The first test is an omitted predictor test with the propensity score as the omitted variable. In the case of rejection we cannot distinguish between rejection due to genuine selection or to misspecification. Thus, we suggest a second test using only individuals with propensity score close to one. The latter relies on an 'identification at infinity' argument, but accommodates cases of irregular identification, and neither of the two tests requires a continuous exclusion restriction. We apply our procedure to test for selection in log hourly wages using UK survey data and derive an extension of the tests to the conditional mean.
Keywords:
Conditional quantile function
irregular identification
nonparametric estimation
specification test
Journal
IF:
7
Papers:
565
Citations:
2.3K
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Cited Papers
QUANTILE SELECTION MODELS WITH AN APPLICATION TO UNDERSTANDING CHANGES IN WAGE INEQUALITY.png)
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