SOME CONVERGENCE THEORY FOR ITERATIVE ESTIMATION PROCEDURES WITH AN APPLICATION TO SEMIPARAMETRIC ESTIMATION

DOI10.1017/S0266466605050425zbMath1096.62026OpenAlexW2028146138MaRDI QIDQ3377455

Jeff Dominitz, Robert P. Sherman

Publication date: 22 March 2006

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

Full work available at URL: https://doi.org/10.1017/s0266466605050425

zbMATH Keywords

iterative estimationbinary response modelfinite-dimensional parameters

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Central limit and other weak theorems (60F05)

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Single index regression models in the presence of censoring depending on the covariates ⋮ Efficient two-step estimation via targeting ⋮ Estimation and forecasting in vector autoregressive moving average models for rich datasets ⋮ Current status linear regression ⋮ Local consistency of the iterative least-squares estimator for the semiparametric binary choice model ⋮ A \(q\)-exponential regression model ⋮ Conditional density estimation in a censored single-index regression model ⋮ A SIMPLE ITERATIVE Z-ESTIMATOR FOR SEMIPARAMETRIC MODELS ⋮ Recursive estimation in large panel data models: theory and practice ⋮ Single-index copulas ⋮ The time-varying GARCH-in-mean model ⋮ Flexible bivariate Poisson integer-valued GARCH model ⋮ Single-index regression models with right-censored responses ⋮ How Many Iterations are Sufficient for Efficient Semiparametric Estimation?

Cites Work

• Asymptotic efficiency in semi-parametric models with censoring
• The Stochastic Difference Between Econometric Statistics
• Simulation and the Asymptotics of Optimization Estimators
• Efficiency Bounds for Distribution-Free Estimators of the Binary Choice and the Censored Regression Models
• A Smoothed Maximum Score Estimator for the Binary Response Model
• One-Step Huber Estimates in the Linear Model
• An Efficient Semiparametric Estimator for Binary Response Models

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