Nonparametric Inference Based on Conditional Moment Inequalities
Donald Andrews () and
Xiaoxia Shi ()
No 1840R, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University
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 Cramer-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 (search for similar items in EconPapers)
JEL-codes: C12 C15 (search for similar items in EconPapers)
Date: 2011-12, Revised 2013-02
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Journal Article: Nonparametric inference based on conditional moment inequalities (2014)
Working Paper: Nonparametric Inference Based on Conditional Moment Inequalities (2013)
Working Paper: Nonparametric Inference Based on Conditional Moment Inequalities (2011)
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