Estimation and Inference for Distribution Functions and Quantile Functions in Treatment Effect Models

Stephen G. Donald () and Yu-Chin Hsu
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Stephen G. Donald: Department of Economics, University of Texas at Austin

No 12-A016, IEAS Working Paper : academic research from Institute of Economics, Academia Sinica, Taipei, Taiwan

Abstract: We propose inverse probability weighted estimators for the distribution functions of the potential outcomes of a binary treatment under the unconfoundedness assumption. We also apply the inverse mapping on the distribution functions to obtain the quantile functions. We show that the proposed estimators converge weakly to zero mean Gaussian processes. A simulation method based on the multiplier central limit theorem is proposed to approximate these limiting Gaussian processes. The estimators in the treated subpopulation are shown to share the same properties. To demonstrate the usefulness of our results, we construct Kolmogorov-Smirnov type tests for stochastic dominance relations between the distributions of potential outcomes. We examine the finite sample properties of our tests in a set of Monte-Carlo simulations and use our tests in an empirical example which shows 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)
Pages: 43 pages
Date: 2012-12
New Economics Papers: this item is included in nep-ecm
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Related works:
Journal Article: Estimation and inference for distribution functions and quantile functions in treatment effect models (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:sin:wpaper:12-a016

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