GMM estimation in partial linear models with endogenous covariates causing an over-identified problem

DOI10.1080/03610926.2014.901363zbMath1342.62057OpenAlexW2339498100MaRDI QIDQ3178629

Hua Liang, Yong Zhou, Baicheng Chen

Publication date: 15 July 2016

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2014.901363

zbMATH Keywords

endogeneityinstrumental variablespresmoothingmoment restrictionpenalized GMMprofiled least square

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) General nonlinear regression (62J02)

Related Items (4)

Regularization statistical inferences for partially linear models with high dimensional endogenous covariates ⋮ Adjusted empirical likelihood inferences for varying coefficient partially non linear models with endogenous covariates ⋮ Double penalized regularization estimation for partially linear instrumental variable models with ultrahigh dimensional instrumental variables ⋮ Regularizing Double Machine Learning in Partially Linear Endogenous Models

Uses Software

• Stata

Cites Work

• Large Sample Properties of Generalized Method of Moments Estimators
• The Adaptive Lasso and Its Oracle Properties
• Automatic model selection for partially linear models
• SCAD-penalized regression in high-dimensional partially linear models
• Convergence rates for parametric components in a partly linear model
• Nonparametric regression with errors in variables
• Estimation in a semiparametric partially linear errors-in-variables model
• Asymptotic properties of bridge estimators in sparse high-dimensional regression models
• Profiled forward regression for ultrahigh dimensional variable screening in semiparametric partially linear models
• Partially linear models with missing response variables and error-prone covariates
• LASSO-TYPE GMM ESTIMATOR
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Kriging with Nonparametric Variance Function Estimation
• Variable Selection for Partially Linear Models With Measurement Errors
• Nonconcave Penalized Likelihood With NP-Dimensionality
• Regularization and Variable Selection Via the Elastic Net
• Measurement Error in Nonlinear Models
• Semiparametric Regression Analysis With Missing Response at Random
• Estimation in Partially Linear Models With Missing Covariates

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