期刊
JOURNAL OF ECONOMETRICS
卷 179, 期 1, 页码 31-45出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2013.10.005
关键词
Asymptotic size; Kernel; Local power; Moment inequalities; Nonparametric inference; Partial identification
资金
- National Science Foundation [SES-0751517, SES-1058376]
- Direct For Social, Behav & Economic Scie
- Divn Of Social and Economic Sciences [1058376] Funding Source: National Science Foundation
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. (C) 2013 Elsevier B.V. All rights reserved.
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