This paper examines the distribution structural functions (DSFs) and quantile structural functions (QSFs) in a semiparametric treatment effect model. The DSF and QSF are defined as the distribution function and quantile function of the counterfactural outcome when covariates are exogenously...

This paper considers methods for comparing poverty in two income distributions. We …first discuss the concept and usefulness of the Poverty Gap Pro…le (PGP) for comparing poverty in two populations. Dominance of one PGP over another suggests poverty dom- inance for a wide class of indices...

In this paper, we propose a Vuong (1989)-type model selection test for conditional moment inequality models. The test uses a new average generalized empirical likelihood (AGEL) criterion function designed to incorporate full restriction of the conditional model. We also introduce a new...

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...

We extend Hansen’s (2005) recentering method to a continuum of inequality constraints to construct new Kolmogorov-Smirnov tests for stochastic dominance of any pre-specified order. We show that our tests have correct size asymptotically, are consistent against fixed alternatives and are...

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...

We construct a Kolmogorov-Smirnov test for the null hypothesis that the conditional average treatment effect is non-negative conditional on every possible value of the covariates. The null hypothesis can be characterized as a conditional moment inequality under the unconfoundedness assumption,...

This paper extends Kiefer, Vogelsang, and Bunzel (2000) and Kiefer and Vogelsang (2002b) to propose a class of over-identifying restrictions (OIR) tests that are robust to heteroskedasticity and serial correlations of unknown form. These OIR tests do not require consistent estimation of the...

We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture heterogeneity of a treatment effect across subpopulations when the unconfoundedness assumption applies. In contrast to quantile regressions, the subpopulations of interest are defined...

We extend Hansen’s (2005) test to testing hypotheses involving general inequality constraints where the variance–covariance matrix of the functions in the constraints depends on the unknown parameters. The test can be applied to a wider class of problems than Wolak’s (1991).