Antoine, Bertille and Lavergne, Pascal (2014) Conditional moments models under semi-strong identification. Journal of Econometrics, vol. 182 (n° 3). pp. 59-69.

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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.

Item Type: Article
Language: English
Date: September 2014
Refereed: Yes
Uncontrolled Keywords: Identification, Conditional moments, Minimum distance estimation
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 16 Mar 2015 14:52
Last Modified: 02 Apr 2021 15:49
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