SMALL BANDWIDTH ASYMPTOTICS FOR DENSITY-WEIGHTED AVERAGE DERIVATIVES

DOI10.1017/S0266466613000169zbMath1301.62035OpenAlexW3121913534MaRDI QIDQ4979938

Michael Jansson, Richard K. Crump, Matias D. Cattaneo

Publication date: 20 June 2014

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

Full work available at URL: https://doi.org/10.1017/s0266466613000169

zbMATH Keywords

kernel estimatorsasymptotic efficiencyMonte Carlo experimentvariance estimationhigher-order kerneloptimal bandwidth choicenonparametric functional estimationdensity-weighted average derivative estimator

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Nonparametric robustness (62G35) Nonparametric estimation (62G05) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (16)

AVERAGE DENSITY ESTIMATORS: EFFICIENCY AND BOOTSTRAP CONSISTENCY ⋮ NONPARAMETRIC WEIGHTED AVERAGE QUANTILE DERIVATIVE ⋮ Estimation and inference about tail features with tail censored data ⋮ BOOTSTRAPPING DENSITY-WEIGHTED AVERAGE DERIVATIVES ⋮ Minimizing sensitivity to model misspecification ⋮ ASYMPTOTICALLY EFFICIENT ESTIMATION OF WEIGHTED AVERAGE DERIVATIVES WITH AN INTERVAL CENSORED VARIABLE ⋮ Diagnostic Testing of Finite Moment Conditions for the Consistency and Root-N Asymptotic Normality of the GMM and M Estimators ⋮ Empirical Likelihood and Uniform Convergence Rates for Dyadic Kernel Density Estimation ⋮ Kernel density estimation for undirected dyadic data ⋮ ALTERNATIVE ASYMPTOTICS AND THE PARTIALLY LINEAR MODEL WITH MANY REGRESSORS ⋮ Classical Laplace estimation for \(\root3\of n\)-consistent estimators: improved convergence rates and rate-adaptive inference ⋮ Robust inference on average treatment effects with possibly more covariates than observations ⋮ Smoothness adaptive average derivative estimation ⋮ A NONPARAMETRIC TEST OF SIGNIFICANT VARIABLES IN GRADIENTS ⋮ AVERAGE DERIVATIVE ESTIMATION UNDER MEASUREMENT ERROR ⋮ LIMIT THEOREMS FOR FACTOR MODELS

Cites Work

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