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Semiparametric Estimation with Generated Covariates

Listed author(s):
  • Enno Mammen
  • Christoph Rothe
  • Melanie Schienle

In this paper, we study a general class of semiparametric optimization estimators of a vector-valued parameter. The criterion function depends on two types of in nite-dimensional nuisance parameters: a conditional expectation function that has been estimated nonparametrically using generated covariates, and another estimated function that is used to compute the generated covariates in the first place. We study the asymptotic properties of estimators in this class, which is a nonstandard problem due to the presence of generated covariates. We give conditions under which estimators are root-n consistent and asymptotically normal, and derive a general formula for the asymptotic variance.

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File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2011-064.pdf
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Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2011-064.

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Length: 47 pages
Date of creation: Oct 2011
Handle: RePEc:hum:wpaper:sfb649dp2011-064
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