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Endogeneity in nonparametric and semiparametric regression models

Authors: Richard Blundell and James L. Powell
Date: 01 November 2001
Type: cemmap Working Paper, CWP09/01
DOI: 10.1920/wp.cem.2001.0901

Abstract

This paper considers the nonparametric and semiparametric methods for estimating regression models with continuous endogenous regressors. We list a number of different generalizations of the linear structural equation model, and discuss how two common estimation approaches for linear equations-the "instrumental variables" and "control function" approaches-may be extended to nonparametric generalizations of the linear model and to their semiparametric variants. We consider the identification and estimation of the "Average Structural Function" and argue that this is a parameter of central interest in the analysis of semiparametric and nonparametric models with endogenous regressors. We consider a particular semiparametric model, the binary response model with linear index function and nonparametric error distribution, and describes in detail how estimation of the parameters of interest can be constructed using the "control function" approach. This estimator is applied to estimating the relation of labor force participation to nonlabor income, viewed as an endogenous regressor.

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Now published:
Richard Blundell and James L. Powell April 2003, Endogeneity in nonparametric and semiparametric regression models, Journal article, Cambridge University Press

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