Business Analytics

GMM with Multiple Missing Variables

Assistant Professor Saraswata Chaudhuri; Department of Economics, Authors: Saraswata Chaudhuri and David Guilkey; University of North Carolina at Chapel Hill

8th Mar 2013  11:00 am - Room 498

Abstract:  We consider estimation of a finite dimensional unknown parameter value defined by a set of overidentifying unconditional moment restrictions. Random variables forming the different elements of the moment vector can be missing at random -- jointly or individually -- for some sample units, thus rendering the corresponding elements of the moment vector infeasible.  We obtain the semiparametric efficiency bound under this setup. We recommend semiparametric estimators that utilize the available information optimally and hence have asymptotic variances equal to the efficiency bound. A small scale Monte-Carlo experiment provides evidence that these semiparametric estimators perform better than the existing estimators even in relatively small samples. An empirical example studying the relationship between a child's years of schooling and number of siblings based on data from Indonesia is provided for the purpose of illustration.

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