AN INTEGRATED KERNEL-WEIGHTED SMOOTHED MAXIMUM SCORE ESTIMATOR FOR THE PARTIALLY LINEAR BINARY RESPONSE MODEL

DOI10.1017/S0266466613000431zbMath1296.62090MaRDI QIDQ3191833

Jerome M. Krief

Publication date: 25 September 2014

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

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Linear regression; mixed models (62J05)

Related Items (5)

Relaxing conditional independence in an endogenous binary response model ⋮ Binary quantile regression with local polynomial smoothing ⋮ A data-driven bandwidth selection method for the smoothed maximum score estimator ⋮ Smoothed maximum score estimation with nonparametrically generated covariates ⋮ Maximum simulated likelihood estimation of the panel sample selection model

Cites Work

• Smooth optimum kernel estimators of densities, regression curves and modes
• Nonparametric estimates of regression quantiles and their local Bahadur representation
• On maximum likelihood estimation in infinite dimensional parameter spaces
• Cube root asymptotics
• Semiparametric analysis of discrete response. Asymptotic properties of the maximum score estimator
• Limited information estimators and exogeneity tests for simultaneous probit models
• On average derivative quantile regression
• Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables
• Endogenous binary choice model with median restrictions
• Semiparametric estimation of binary response models with endogenous regressors
• Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals
• Simulation and the Asymptotics of Optimization Estimators
• An Exogeneity Test for a Simultaneous Equation Tobit Model with an Application to Labor Supply
• A Smoothed Maximum Score Estimator for the Binary Response Model
• Endogeneity in Semiparametric Binary Response Models
• Semiparametric Estimation of a Regression Model with an Unknown Transformation of the Dependent Variable
• Maximization by Quadratic Hill-Climbing

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