Kernel-Based Semiparametric Estimators: Small Bandwidth Asymptotics and Bootstrap Consistency

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Kernel-Based Semiparametric Estimators: Small Bandwidth Asymptotics and Bootstrap Consistency. / Cattaneo, Matias D.; Jansson, Michael.

In: Econometrica, Vol. 86, No. 3, 2018, p. 955-995.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Bibtex

@article{b408cb3d880d4282adbd84135fe45ddc,

title = "Kernel-Based Semiparametric Estimators: Small Bandwidth Asymptotics and Bootstrap Consistency",

abstract = "This paper develops asymptotic approximations for kernel-based semiparametric estimators under assumptions accommodating slower-than-usual rates of convergence of their nonparametric ingredients. Our first main result is a distributional approximation for semiparametric estimators that differs from existing approximations by accounting for a bias. This bias is nonnegligible in general, and therefore poses a challenge for inference. Our second main result shows that some (but not all) nonparametric bootstrap distributional approximations provide an automatic method of correcting for the bias. Our general theory is illustrated by means of examples and its main finite sample implications are corroborated in a simulation study.",

keywords = "Semiparametrics, small bandwidth asymptotics, bootstrapping, robust inference",

author = "Cattaneo, {Matias D.} and Michael Jansson",

year = "2018",

doi = "10.3982/ECTA12701",

language = "English",

volume = "86",

pages = "955--995",

journal = "Econometrica",

issn = "0012-9682",

publisher = "Wiley-Blackwell Publishing Ltd.",

number = "3",

}

RIS

TY - JOUR

T1 - Kernel-Based Semiparametric Estimators: Small Bandwidth Asymptotics and Bootstrap Consistency

AU - Cattaneo, Matias D.

AU - Jansson, Michael

PY - 2018

Y1 - 2018

N2 - This paper develops asymptotic approximations for kernel-based semiparametric estimators under assumptions accommodating slower-than-usual rates of convergence of their nonparametric ingredients. Our first main result is a distributional approximation for semiparametric estimators that differs from existing approximations by accounting for a bias. This bias is nonnegligible in general, and therefore poses a challenge for inference. Our second main result shows that some (but not all) nonparametric bootstrap distributional approximations provide an automatic method of correcting for the bias. Our general theory is illustrated by means of examples and its main finite sample implications are corroborated in a simulation study.

AB - This paper develops asymptotic approximations for kernel-based semiparametric estimators under assumptions accommodating slower-than-usual rates of convergence of their nonparametric ingredients. Our first main result is a distributional approximation for semiparametric estimators that differs from existing approximations by accounting for a bias. This bias is nonnegligible in general, and therefore poses a challenge for inference. Our second main result shows that some (but not all) nonparametric bootstrap distributional approximations provide an automatic method of correcting for the bias. Our general theory is illustrated by means of examples and its main finite sample implications are corroborated in a simulation study.

KW - Semiparametrics

KW - small bandwidth asymptotics

KW - bootstrapping

KW - robust inference

U2 - 10.3982/ECTA12701

DO - 10.3982/ECTA12701

M3 - Journal article

VL - 86

SP - 955

EP - 995

JO - Econometrica

JF - Econometrica

SN - 0012-9682

IS - 3

ER -

Department of Economics and Business Economics