期刊名称：COWLES Foundation Discussion Paper / Cowles Foundation for Research in Economics
出版年度：2013
卷号：2013
期号：1
出版社：Yale University
摘要：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.
关键词：Asymptotic size; Kernel; Local power; Moment inequalities; Nonparametric inference; Partial identification
