Inference Based on Conditional Moment Inequalities

Authors

Donald W.K. Andrews
Xiaoxia Shi

Document Type

Discussion Paper

Publication Date

6-1-2010

CFDP Number

1761R2

CFDP Revision Date

2012-05-01

CFDP Pages

67

Abstract

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.

Recommended Citation

Andrews, Donald W.K. and Shi, Xiaoxia, "Inference Based on Conditional Moment Inequalities" (2010). Cowles Foundation Discussion Papers. 2094.
https://elischolar.library.yale.edu/cowles-discussion-paper-series/2094