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Semiparametric Estimation with Generated Covariates

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  • Enno Mammen
  • Christoph Rothe
  • Melanie Schienle

Abstract

We study a general class of semiparametric estimators when the in nite-dimensional nuisance parameters include a conditional expectation function that has been estimated nonparametri- cally using generated covariates. Such estimators are used frequently to e.g. estimate nonlinear models with endogenous covariates when identi cation is achieved using control variable tech- niques. We study the asymptotic properties of estimators in this class, which is a non-standard problem due to the presence of generated covariates. We give conditions under which estimators are root-n consistent and asymptotically normal, derive a general formula for the asymptotic variance, and show how to establish validity of the bootstrap.

Suggested Citation

  • Enno Mammen & Christoph Rothe & Melanie Schienle, 2014. "Semiparametric Estimation with Generated Covariates," SFB 649 Discussion Papers SFB649DP2014-043, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    • Handle: RePEc:hum:wpaper:sfb649dp2014-043
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    Keywords

    Semiparametric estimation; generated covariates; profiling; propensity score;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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