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Author: D'Haultfoeuille, Xavier
Resulting in 2 citations.
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D'Haultfoeuille, Xavier Gaillac, Christophe Maurel, Arnaud |
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Partially Linear Models Under Data Combination Review of Economic Studies published online (29 March 2024): rdae022. Also: https://doi.org/10.1093/restud/rdae022 Cohort(s): NLSY79 Publisher: Oxford University Press Keyword(s): Data Combination; Geometric Properties; Microeconomics, Empirical; Optimal Transport Theory; Partially Linear Model Permission to reprint the abstract has not been received from the publisher. We study partially linear models when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked. This type of data combination problem arises very frequently in empirical microeconomics. Using recent tools from optimal transport theory, we derive a constructive characterization of the sharp identified set. We then build on this result and develop a novel inference method that exploits the specific geometric properties of the identified set. Our method exhibits good performances in finite samples, while remaining very tractable. We apply our approach to study intergenerational income mobility over the period 1850–1930 in the U.S. Our method allows us to relax the exclusion restrictions used in earlier work, while delivering confidence regions that are informative. |
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Bibliography Citation
D'Haultfoeuille, Xavier, Christophe Gaillac and Arnaud Maurel. "Partially Linear Models Under Data Combination." Review of Economic Studies published online (29 March 2024): rdae022.
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D'Haultfoeuille, Xavier Maurel, Arnaud Zhang, Yichong |
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Extremal Quantile Regressions for Selection Models and the Black-White Wage Gap Journal of Econometrics 203,1 (March 2018): 129-142. Also: https://www.sciencedirect.com/science/article/pii/S0304407617302269 Cohort(s): NLSY79, NLSY97 Publisher: Elsevier Keyword(s): Armed Forces Qualifications Test (AFQT); Family Background and Culture; Racial Differences; Wage Gap We consider the estimation of a semiparametric sample selection model without instrument or large support regressor. Identification relies on the independence between the covariates and selection, for arbitrarily large values of the outcome. We propose a simple estimator based on extremal quantile regression and establish its asymptotic normality by extending previous results on extremal quantile regressions to allow for selection. Finally, we apply our method to estimate the black-white wage gap among males from the NLSY79 and NLSY97. We find that premarket factors such as AFQT and family background play a key role in explaining the black-white wage gap. |
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Bibliography Citation
D'Haultfoeuille, Xavier, Arnaud Maurel and Yichong Zhang. "Extremal Quantile Regressions for Selection Models and the Black-White Wage Gap." Journal of Econometrics 203,1 (March 2018): 129-142.
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