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Conditional moment models under semi-strong identification

Bertille Antoine () and Pascal Lavergne

Journal of Econometrics, 2014, vol. 182, issue 1, 59-69

Abstract: 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.

Keywords: Identification; Conditional moments; Minimum distance estimation (search for similar items in EconPapers)
JEL-codes: C13 C12 (search for similar items in EconPapers)
Date: 2014
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Handle: RePEc:eee:econom:v:182:y:2014:i:1:p:59-69