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A note on generalized empirical likelihood estimation of semiparametric conditional moment restriction models. (English) Zbl 1442.62073
Summary: This paper proposes an empirical likelihood-based estimation method for semiparametric conditional moment restriction models, which contain finite dimensional unknown parameters and unknown functions. We extend the results of S. G. Donald et al. [J. Econom. 117, No. 1, 55–93 (2003; Zbl 1022.62046)] by allowing unknown functions to be included in the conditional moment restrictions. We approximate unknown functions by a sieve method and estimate the finite dimensional parameters and unknown functions jointly. We establish consistency and derive the convergence rate of the estimator. We also show that the estimator of the finite dimensional parameters is \(\sqrt n\)-consistent, asymptotically normally distributed, and asymptotically efficient.

62G05 Nonparametric estimation
62F12 Asymptotic properties of parametric estimators
Full Text: DOI
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