Publication Date: June 2010
Revision Date: May 2012
In this paper, we propose an instrumental variable approach to constructing conﬁdence sets (CS’s) for the true parameter in models deﬁned by conditional moment inequalities/equalities. We show that by properly choosing instrument functions, one can transform conditional moment inequalities/equalities into unconditional ones without losing identiﬁcation power. Based on the unconditional moment inequalities/equalities, we construct CS’s by inverting Cramér-von Mises-type or Kolmogorov-Smirnov-type tests. Critical values are obtained using generalized moment selection (GMS) procedures.
We show that the proposed CS’s have correct uniform asymptotic coverage probabilities. New methods are required to establish these results because an inﬁnite-dimensional nuisance parameter aﬀects the asymptotic distributions. We show that the tests considered are consistent against all ﬁxed alternatives and typically have power against n-1/2-local alternatives to some, but not all, sequences of distributions in the null hypothesis. Monte Carlo simulations for ﬁve diﬀerent models show that the methods perform well in ﬁnite samples.
Asymptotic size, Asymptotic power, Conditional moment inequalities, Conﬁdence set, Cramér-von Mises, Generalized moment selection, Kolmogorov-Smirnov, Moment inequalities
JEL Classification Codes: C12, C15
See CFP: 1377