A Simple Data-Driven Estimator for the Semiparametric Sample Selection Model

DOI10.1080/07474938.2014.956577zbMath1491.62208OpenAlexW2011255861MaRDI QIDQ5863561

Lin Zhu, Juan Carlos Escanciano

Publication date: 3 June 2022

Published in: Econometric Reviews (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/07474938.2014.956577

zbMATH Keywords

empirical process theorytwo-step estimatorsemiparametric sample selection models

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Nonparametric regression and quantile regression (62G08) Asymptotic distribution theory in statistics (62E20) Nonparametric estimation (62G05)

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Cites Work

• Two-step series estimation of sample selection models
• Nonparametric/semiparametric estimation and testing of econometric models with data dependent smoothing parameters
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• Optimal smoothing in single-index models
• Uniform convergence of weighted sums of non and semiparametric residuals for estimation and testing
• Uniform in bandwidth consistency of kernel-type function estimators
• Bracketing metric entropy rates and empirical central limit theorems for function classes of Besov- and Sobolev-type
• On Asymptotic Normality of the Local Polynomial Regression Estimator with Stochastic Bandwidths
• Root-N-Consistent Semiparametric Regression
• Nonparametric Estimation of Sample Selection Models
• Semiparametric Estimation of Index Coefficients
• SEMIPARAMETRIC ESTIMATION OF PARTIALLY LINEAR MODELS FOR DEPENDENT DATA WITH GENERATED REGRESSORS
• Asymptotic distribution of data‐driven smoothers in density and regression estimation under dependence
• An Efficient Semiparametric Estimator for Binary Response Models
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