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The semiparametric efficiency bound for models of sequential moment restrictions containing unknown functions

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  • Ai, Chunrong
  • Chen, Xiaohong
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    Abstract

    This paper computes the semiparametric efficiency bound for finite dimensional parameters identified by models of sequential moment restrictions containing unknown functions. Our results extend those of Chamberlain (1992b) and Ai and Chen (2003) for semiparametric conditional moment restrictions with identical information sets to the case of nested information sets, and those of Chamberlain (1992a) and Brown and Newey (1998) for models of sequential moment restrictions without unknown functions to cases with unknown functions of possibly endogenous variables. Our results are applicable to semiparametric panel data models and two stage plug-in problems. As an important example, we compute the efficiency bound for a weighted average derivative of a nonparametric instrumental variables regression (NPIV), and find that simple plug-in NPIV estimators are not efficient. We present an optimally weighted, orthogonalized, sieve minimum distance estimator that achieves the semiparametric efficiency bound.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Econometrics.

    Volume (Year): 170 (2012)
    Issue (Month): 2 ()
    Pages: 442-457

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    Handle: RePEc:eee:econom:v:170:y:2012:i:2:p:442-457

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    Web page: http://www.elsevier.com/locate/jeconom

    Related research

    Keywords: Sequential moment models; Semiparametric efficiency bounds; Optimally weighted orthogonalized sieve minimum distance; Nonparametric IV regression; Weighted average derivatives; Partially linear quantile IV;

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