CFDP 1840

Nonparametric Inference Based on Conditional Moment Inequalities


Publication Date: December 2011

Pages: 67


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.

Supplemental Material: Supplemental material

Supplement pages: 10


Asymptotic size, Kernel, Local power, Moment inequalities, Nonparametric inference, Partial identification

JEL Classification Codes:  C12, C15