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...

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 take the characteristic function approach to goodness-of-fit tests. It has several advantages over existing methods: First, unlike the popular comparison density function approach suggested in Parzen (1979), our approach is applicable to both univariate and multivariate data;...

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....