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July 1, 1988Econometrica2,518 citations

Root-N-Consistent Semiparametric Regression

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PRPeter M. Robinson

Key Points

  • To construct an estimator for unknown slope parameters in semiparametric regression models that avoids misspecification bias while achieving fast, standard parametric convergence rates.
  • Formulated a semiparametric regression framework incorporating an unknown parameter vector and an unspecified arbitrary function.
  • Constructed a plug-in estimator by inserting nonparametric regression estimates into a nonlinear orthogonal projection on conditioning variables.
  • Analyzed asymptotic sampling properties, derived a consistent estimator for the limiting covariance matrix, and evaluated identification and efficiency criteria.
  • Demonstrated that the proposed semiparametric estimator achieves root-N-consistency and asymptotic normality under specified regularity conditions.
  • Derived a consistent estimator of the limiting covariance matrix to enable valid statistical inference.
  • Established theoretical extensions of the semiparametric estimation framework to broader econometric model classes.

Abstract

One type of semiparametric regression is b8X A u(Z), where b and u(Z) are an unknown slope coefficient vector and function. Estimates of b based on incorrect parametrization of u are generally inconsist ent, whereas consistent nonparametric estimates converge slowly. An e stimate, bC, is constructed by inserting nonpar-ametric regression es timates in the nonlinear orthogonal projection on Z. Under regularity conditions bC is shown to be N1/2-consistent for b and asymptoticall y normal, and a consistent estimate of its limiting covariance matrix is given. The author discusses the identification problem and bC's e fficiency. Extensions to other econometric models are described. Copyright 1988 by The Econometric Society.

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Cite This Study

Peter M. Robinson (1988) studied this question.

synapsesocial.com/papers/69d8fa3a2c87b79b92d189d9https://doi.org/10.2307/1912705
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