Article

On the Asymptotic Efficiency of GMM

Marine Carrasco et Jean-Pierre Florens

Résumé

The efficiency of the generalized method of moment (GMM) estimator is addressed by using a characterization of its variance as an inner product in a reproducing kernel Hilbert space. We show that the GMM estimator is asymptotically as efficient as the maximum likelihood estimator if and only if the true score belongs to the closure of the linear space spanned by the moment conditions. This result generalizes former ones to autocorrelated moments and possibly infinite number of moment restrictions. Second, we derive the semiparametric efficiency bound when the observations are known to be Markov and satisfy a conditional moment restriction. We show that it coincides with the asymptotic variance of the optimal GMM estimator, thus extending results by Chamberlain (1987, Journal of Econometrics 34, 305–33) to a dynamic setting. Moreover, this bound is attainable using a continuum of moment conditions.

Remplace

Marine Carrasco et Jean-Pierre Florens, « On the Asymptotic Efficiency of GMM », IDEI Working Paper, n° 173, 2003.

Référence

Marine Carrasco et Jean-Pierre Florens, « On the Asymptotic Efficiency of GMM », Econometric Theory, vol. 30, n° 2, avril 2014, p. 372–406.

Publié dans

Econometric Theory, vol. 30, n° 2, avril 2014, p. 372–406