%0 Journal Article
%@ 0304-4076
%A Antoine, Bertille
%A Lavergne, Pascal
%D 2014
%F publications:16627
%I Elsevier
%J Journal of Econometrics
%K Identification
%K Conditional moments
%K Minimum distance estimation
%N n° 3
%P 59-69
%T Conditional moments models under semi-strong identification
%U https://publications.ut-capitole.fr/id/eprint/16627/
%V vol. 182
%X We consider conditional moment models under semi-strong identification. Identification strength is directly defined through the conditional moments that flatten as the sample size increases. Our new minimum distance estimator is consistent, asymptotically normal, robust to semi-strong identification, and does not rely on the choice of a user-chosen parameter, such as the number of instruments or some smoothing parameter. Heteroskedasticity-robust inference is possible through Wald testing without prior knowledge of the identification pattern. Simulations show that our estimator is competitive with alternative estimators based on many instruments, being well-centered with better coverage rates for confidence intervals.