Document Type
Discussion Paper
Publication Date
1-1-2013
CFDP Number
1885R
CFDP Revision Date
2014-10-01
CFDP Pages
55
Abstract
This paper considers inference for conditional moment inequality models using a multiscale statistic. We derive the asymptotic distribution of this test statistic and use the result to propose feasible critical values that have a simple analytic formula, and to prove the asymptotic validity of a modified bootstrap procedure. The asymptotic distribution is extreme value, and the proof uses new techniques to overcome several technical obstacles. The test detects local alternatives that approach the identified set at the best rate in a broad class of models, and is adaptive to the smoothness properties of the data generating process. Our results also have implications for the use of moment selection procedures in this setting. We provide a monte carlo study and an empirical illustration to inference in a regression model with endogenously censored and missing data.
Recommended Citation
Armstrong, Timothy B. and Chan, Hock Peng, "Multiscale Adaptive Inference on Conditional Moment Inequalities" (2013). Cowles Foundation Discussion Papers. 2254.
https://elischolar.library.yale.edu/cowles-discussion-paper-series/2254