Semiparametric Varying Coefficient Models with Endogenous Covariates
Samuele Centorrino, Jeffrey S. Racine
Abstract
Open-access reader
Samuele Centorrino, Jeffrey S. Racine
Abstract
Open-access reader
Though parametric methods are popular in applied settings, practitioners often require nonparametric alternatives.However, fully nonparametric methods are known to suffer from the curse-of-dimensionality, which limits their practical application. Semiparametric methods occupy a middle ground, have the desirable feature that they are both flexible, and provide an attractive alternative to fully nonparametric methods, while attenuating the curse-of-dimensionality.Traditional semiparametric methods, such as the popular 'varying coefficient' specification, do not account for endogenous covariates, which restricts their application. In this paper we consider the estimation of semiparametric varying coefficient models when the functional coefficients may contain (continuous) endogenous covariates thereby extending the reach of this fl exible and powerful class of models.
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Though parametric methods are popular in applied settings, practitioners often require nonparametric alternatives.However, fully nonparametric methods are known to suffer from the curse-of-dimensionality, which limits their practical application. Semiparametric methods occupy a middle ground, have the desirable feature that they are both flexible, and provide an attractive alternative to fully nonparametric methods, while attenuating the curse-of-dimensionality.Traditional semiparametric methods, such as the popular 'varying coefficient' specification, do not account for endogenous covariates, which restricts their application. In this paper we consider the estimation of semiparametric varying coefficient models when the functional coefficients may contain (continuous) endogenous covariates thereby extending the reach of this fl exible and powerful class of models.
Key concepts: Nonparametric statistics, Semiparametric regression, Covariate, Curse of dimensionality, Semiparametric model, Econometrics, Parametric statistics, Computer science