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Nonparametric Inference Based on Conditional Moment Inequalities

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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 Cramer-von-Mises-type test statistic and employs a generalized moment selection critical value.

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  • Donald W.K. Andrews & Xiaoxia Shi, 2011. "Nonparametric Inference Based on Conditional Moment Inequalities," Cowles Foundation Discussion Papers 1840R, Cowles Foundation for Research in Economics, Yale University, revised Feb 2013.
    • Handle: RePEc:cwl:cwldpp:1840r
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    File URL: https://cowles.yale.edu/sites/default/files/files/pub/d18/d1840-ra.pdf
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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