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Martin Huber
Former Member
Title
Prof. Ph.D.
Last Name
Huber
First name
Martin
Phone
+41 71 224 2300
Google Scholar
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1 - 3 of 3
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PublicationA test of the conditional independence assumption in sample selection modelsIdentification 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 articleJournal: Journal of Applied EconometricsIssue: forthcoming
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PublicationTreatment Evaluation in the Presence of Sample SelectionSample selection and attrition are inherent in a range of treatment evaluation problems such as the estimation of the returns to schooling or training. Conventional estimators tackling selection bias typically rely on restrictive functional form assumptions that are unlikely to hold in reality. This paper shows identification of average and quantile treatment effects in the presence of the double selection problem (i) into a selective subpopulation (e.g., working - selection on unobservables) and (ii) into a binary treatment (e.g., training - selection on observables) based on weighting observations by the inverse of a nested propensity score that characterizes either selection probability. Root-n-consistent weighting estimators based on parametric propensity score models are applied to female labor market data to estimate the returns to education.Type: journal articleJournal: Econometric ReviewsVolume: 2013Issue: forthcoming
Scopus© Citations 15 -
PublicationTesting exclusion restrictions and additive separability in sample selection modelsStandard sample selection models with non-randomly censored outcomes assume (i) an exclusion restriction (i.e., a variable affecting selection, but not the outcome) and (ii) additive separability of the errors in the selection process. This paper proposes tests for the joint satisfaction of these assumptions by applying the approach of Huber and Mellace (2011) (for testing instrument validity under treatment endogeneity) to the sample selection framework. We show that the exclusion restriction and additive separability imply two testable inequality constraints that come from both point identifying and bounding the outcome distribution of the subpopulation that is always selected/observed. We apply the tests to two variables for which the exclusion restriction is frequently invoked in female wage regressions: non-wife/husband's income and the number of (young) children. Considering eight empirical applications, our results suggest that the identifying assumptions are likely violated for the former variable, but cannot be refuted for the latter on statistical grounds.Type: journal articleJournal: Empirical EconomicsVolume: 2014Issue: forthcoming - online seit 09.13
Scopus© Citations 34