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Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions Author info | Abstract | Publisher info | Download info | Related research | Statistics Chunrong Ai
Xiaohong Chen
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We propose an estimation method for models of conditional moment restrictions, which contain finite dimensional unknown parameters (theta) and infinite dimensional unknown functions (h). Our proposal is to approximate h with a sieve and to estimate theta and the sieve parameters jointly by applying the method of minimum distance. We show that: (i) the sieve estimator of h is consistent with a rate faster than n-super--1/4 under certain metric; (ii) the estimator of theta is root-n consistent and asymptotically normally distributed; (iii) the estimator for the asymptotic covariance of the theta estimator is consistent and easy to compute; and (iv) the optimally weighted minimum distance estimator of theta attains the semiparametric efficiency bound. We illustrate our results with two examples: a partially linear regression with an endogenous nonparametric part, and a partially additive IV regression with a link function. Copyright The Econometric Society 2003.
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Article provided by Econometric Society in its journal Econometrica .
Volume (Year): 71 (2003)
Issue (Month): 6 (November)
Pages: 1795-1843
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Handle: RePEc:ecm:emetrp:v:71:y:2003:i:6:p:1795-1843Contact details of provider: Phone: 1 212 998 3820 Fax: 1 212 995 4487 Email: Web page: http://www.econometricsociety.org/ More information through EDIRC
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