This article proposes testing the hypothesis of a uniformly non-positive nonparametric regression function using a test statistic with tabulated critical values. The none hypothesis is characterized in terms of the significance of a parameter, which measures a distance from the double-integrated regression function to the class of concave functions. The test statistic is a suitably scaled parameter estimate, which does not require smooth estimation of the underlying regression and/or the conditional variance functions. The finite sample performance of the proposed test is studied by means of two Monte Carlo experiments, showing that the proposed method compares favorably to existing procedures. (C) 2015 Elsevier B.V. All rights reserved
This article proposes bootstrap-based stochastic dominance tests for nonparametric conditional dist...
This article proposes bootstrap-based stochastic dominance tests for nonparametric conditional dist...
In this paper I present a novel approach to inference in models where the partially identified param...
This article proposes testing the hypothesis of a uniformly non-positive nonparametric regression fu...
This article proposes testing the hypothesis of a uniformly non-positive nonparametric regression fu...
This article proposes testing the hypothesis of a uniformly non-positive nonparametric regression fu...
We propose two classes of consistent tests in parametric econometric models defined through multiple...
We propose two classes of consistent tests in parametric econometric models defined through multiple...
We propose two classes of consistent tests in parametric econometric models defined through multiple...
This paper develops methods of inference for nonparametric and semiparametric parameters defined by c...
This paper develops methods of inference for nonparametric and semiparametric parameters defined by c...
This paper develops methods of inference for nonparametric and semiparametric parameters defined by c...
This paper develops methods of inference for nonparametric and semiparametric parameters defined by c...
We propose non-nested hypotheses tests for conditional moment restriction models based on the method...
We propose non-nested hypotheses tests for conditional moment restriction models based on the method...
This article proposes bootstrap-based stochastic dominance tests for nonparametric conditional dist...
This article proposes bootstrap-based stochastic dominance tests for nonparametric conditional dist...
In this paper I present a novel approach to inference in models where the partially identified param...
This article proposes testing the hypothesis of a uniformly non-positive nonparametric regression fu...
This article proposes testing the hypothesis of a uniformly non-positive nonparametric regression fu...
This article proposes testing the hypothesis of a uniformly non-positive nonparametric regression fu...
We propose two classes of consistent tests in parametric econometric models defined through multiple...
We propose two classes of consistent tests in parametric econometric models defined through multiple...
We propose two classes of consistent tests in parametric econometric models defined through multiple...
This paper develops methods of inference for nonparametric and semiparametric parameters defined by c...
This paper develops methods of inference for nonparametric and semiparametric parameters defined by c...
This paper develops methods of inference for nonparametric and semiparametric parameters defined by c...
This paper develops methods of inference for nonparametric and semiparametric parameters defined by c...
We propose non-nested hypotheses tests for conditional moment restriction models based on the method...
We propose non-nested hypotheses tests for conditional moment restriction models based on the method...
This article proposes bootstrap-based stochastic dominance tests for nonparametric conditional dist...
This article proposes bootstrap-based stochastic dominance tests for nonparametric conditional dist...
In this paper I present a novel approach to inference in models where the partially identified param...