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2014•Oxford University Press eBooksRequires access

Nonparametric and Semiparametric Estimation of a Set of Regression Equations

Jeffrey S. Racine, Liangjun Su, Aman Ullah, Aman Ullah, Yun Wang

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Abstract

The objective of this chapter is to review some of the recent developments on estimating a set of regression equations within nonparametric and semiparametric framework. The procedures of estimation for various nonparametric and semiparametric SRE models are also proposed, such as the partially linear semiparametric model, the model with nonparametric autocorrelated errors, the additive nonparametric model, the varying coefficient model, and the model with endogeneity.

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What this paper is about

The objective of this chapter is to review some of the recent developments on estimating a set of regression equations within nonparametric and semiparametric framework. The procedures of estimation for various nonparametric and semiparametric SRE models are also proposed, such as the partially linear semiparametric model, the model with nonparametric autocorrelated errors, the additive nonparametric model, the varying coefficient model, and the model with endogeneity.

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Available abstract

The objective of this chapter is to review some of the recent developments on estimating a set of regression equations within nonparametric and semiparametric framework. The procedures of estimation for various nonparametric and semiparametric SRE models are also proposed, such as the partially linear semiparametric model, the model with nonparametric autocorrelated errors, the additive nonparametric model, the varying coefficient model, and the model with endogeneity.

Key concepts: Semiparametric regression, Nonparametric statistics, Semiparametric model, Nonparametric regression, Econometrics, Endogeneity, Mathematics, Statistics

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