Asymptotic Efficiency of Semiparametric Two-step GMM

• Daniel Ackerberg ^[1] ; Xiaohong Chen ^[2] ; Jinyong Hahn ^[3] ; Zhipeng Liao ^[3]
  1. [1] University of Michigan
  2. [2] Yale University
  3. [3] UCLA

  Mostrar afiliaciones +
• Localización: Review of economic studies, ISSN 0034-6527, Vol. 81, Nº 3, 2014, págs. 919-943
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Many structural economics models are semiparametric ones in which the unknown nuisance functions are identified via non-parametric conditional moment restrictions with possibly non-nested or overlapping conditioning sets, and the finite dimensional parameters of interest are over-identified via unconditional moment restrictions involving the nuisance functions. In this article we characterize the semiparametric efficiency bound for this class of models. We show that semiparametric two-step optimally weighted GMM estimators achieve the efficiency bound, where the nuisance functions could be estimated via any consistent non-parametric methods in the first step. Regardless of whether the efficiency bound has a closed form expression or not, we provide easy-to-compute sieve-based optimal weight matrices that lead to asymptotically efficient two-step GMM estimators.