Nonparametric Inference Based on Conditional Moment Inequalities

100 Pages Posted: 20 Feb 2013

See all articles by Donald W. K. Andrews

Donald W. K. Andrews

Yale University - Cowles Foundation

Xiaoxia Shi

University of Wisconsin - Madison; Yale University

Multiple version iconThere are 3 versions of this paper

Date Written: February 20, 2013

Abstract

This paper develops methods of inference for nonparametric and semiparametric parameters defined by conditional moment inequalities and/or equalities. The parameters need not be identified. Confidence sets and tests are introduced. The correct uniform asymptotic size of these procedures is established. The false coverage probabilities and power of the CS's and tests are established for fixed alternatives and some local alternatives. Finite-sample simulation results are given for a nonparametric conditional quantile model with censoring and a nonparametric conditional treatment effect model. The recommended CS/test uses a Cramér-von-Mises-type test statistic and employs a generalized moment selection critical value.

Keywords: asymptotic size, kernel, local power, moment inequalities, nonparametric inference, partial identification

JEL Classification: C12, C15

Suggested Citation

Andrews, Donald W. K. and Shi, Xiaoxia, Nonparametric Inference Based on Conditional Moment Inequalities (February 20, 2013). Cowles Foundation Discussion Paper No. 1840R, Available at SSRN: https://ssrn.com/abstract=2221362 or http://dx.doi.org/10.2139/ssrn.2221362

Donald W. K. Andrews (Contact Author)

Yale University - Cowles Foundation ( email )

Box 208281
New Haven, CT 06520-8281
United States
203-432-3698 (Phone)
203-432-6167 (Fax)

Xiaoxia Shi

University of Wisconsin - Madison ( email )

1180 Observatory Drive
Madison, WI 53706
United States

Yale University

28 Hillhouse Ave
New Haven, CT 06520-8268
United States

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