On Deconvolution as a First Stage Nonparametric Estimator

DOI10.1080/07474930903559276zbMath1192.62117OpenAlexW3124795089MaRDI QIDQ3578995

Yingyao Hu, Geert Ridder

Publication date: 5 August 2010

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

Full work available at URL: https://doi.org/10.1080/07474930903559276

zbMATH Keywords

asymptotic normalitydeconvolutionnonparametric estimationmeasurement error modelbounded supportordinary smooth

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) General nonlinear regression (62J02)

Related Items (8)

Injectivity of a class of integral operators with compactly supported kernels ⋮ Prices, profits, proxies, and production ⋮ Time-varying unobserved heterogeneity in earnings shocks ⋮ Well-posedness of measurement error models for self-reported data ⋮ Uniform confidence bands for nonparametric errors-in-variables regression ⋮ Uniform confidence bands in deconvolution with unknown error distribution ⋮ ESTIMATING NONLINEAR STRUCTURAL RELATIONSHIPS ⋮ NONLINEAR PANEL DATA MODELS WITH DISTRIBUTION-FREE CORRELATED RANDOM EFFECTS

Cites Work

• Unnamed Item
• On the optimal rates of convergence for nonparametric deconvolution problems
• Nonparametric estimation of the measurement error model using multiple indicators.
• Semi-parametric estimation in the nonlinear structural errors-in-variables model
• Robust and consistent estimation of nonlinear errors-in-variables models
• The Asymptotic Variance of Semiparametric Estimators
• Semiparametric Estimation of Regression Models for Panel Data
• Estimation of Nonlinear Models with Measurement Error
• Measurement Error Models with Auxiliary Data
• Convergence of stochastic processes

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