We derive optimal bandwidths for kernel density estimators of functions of observations proposed in Frees (J. Amer. Statist. Assoc. 89 (1994) 517-525). Our criteria are, respectively, the minimization of the asymptotic mean squared error and of the asymptotic mean integrated squared error of the...

In this paper, we first re-visit the inference problem for interval identified parameters originally studied in Imbens and Manski (2004) and later extended in Stoye (2008). We take the general criterion function approach and establish a new confidence interval that is asymptotically valid under...

In this paper, we study partial identification of the distribution of treatment effects of a binary treatment for ideal randomized experiments, ideal randomized experiments with a known value of a dependence measure, and for data satisfying the selection-on-observables assumption respectively....

In this paper, we supplement the identification results for the mean treatment effect for the treated in the difference-in-differences framework studied by Abadie (2005) by establishing partial identification results for the distribution and quantile of the counterfactual outcome and of the...

This paper develops a consistent test for the correct hazard rate specification within the context of random right hand censoring of the dependent variable. The test is based on comparing a parametric estimate with a kernel estimate of the hazard rate. We establish the asymptotic distribution of...

We propose a data-driven test for dispersive ordering and show that the asymptotic null distribution of the data-driven test is the standard normal. Our simulations confirm the efficacy of this test in moderately large samples.

By using nonparametric kernel estimation method and a central limit theorem for degenerate U-statistics of order higher than two, the authors develop several consistent model specification tests in the context of a nonparametric regression model. These include tests for omitted variables, tests...

In this paper, we propose a new class of asymptotically efficient estimators for moment condition models. These estimators share the same higher order bias properties as the generalized empirical likelihood estimators and once bias corrected, have the same higher order efficiency properties as...