Estimation and inference for distribution functions and quantile functions in treatment effect models

Stephen G. Donald and Yu-Chin Hsu

Journal of Econometrics, 2014, vol. 178, issue P3, 383-397

Abstract: We propose inverse probability weighted estimators for the distribution functions of the potential outcomes under the unconfoundedness assumption and apply the inverse mapping to obtain the quantile functions. We show that these estimators converge weakly to zero mean Gaussian processes. A simulation method is proposed to approximate these limiting processes. Based on these results, we construct tests for stochastic dominance relations between the potential outcomes. Monte-Carlo simulations are conducted to examine the finite sample properties of our tests. We apply our test in an empirical example and find that a job training program had a positive effect on incomes.

Keywords: Hypothesis testing; Stochastic dominance; Treatment effects; Propensity score (search for similar items in EconPapers)
JEL-codes: C01 C12 C21 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (46)

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Related works:
Working Paper: Estimation and Inference for Distribution Functions and Quantile Functions in Treatment Effect Models (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:178:y:2014:i:p3:p:383-397

DOI: 10.1016/j.jeconom.2013.03.010

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