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Jackknife empirical likelihood: small bandwidth, sparse network and high-dimension asymptotic

Matsushita, Yukitoshi and Otsu, Taisuke (2020) Jackknife empirical likelihood: small bandwidth, sparse network and high-dimension asymptotic. Biometrika. ISSN 0006-3444

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Identification Number: 10.1093/biomet/asaa081

Abstract

This paper sheds light on inference problems for statistical models under alternative or nonstandard asymptotic frameworks from the perspective of jackknife empirical likelihood. Examples include small bandwidth asymptotics for semiparametric inference and goodness-of- fit testing, sparse network asymptotics, many covariates asymptotics for regression models, and many-weak instruments asymptotics for instrumental variable regression. We first establish Wilks’ theorem for the jackknife empirical likelihood statistic on a general semiparametric in- ference problem under the conventional asymptotics. We then show that the jackknife empirical likelihood statistic may lose asymptotic pivotalness under the above nonstandard asymptotic frameworks, and argue that these phenomena are understood as emergence of Efron and Stein’s (1981) bias of the jackknife variance estimator in the first order. Finally we propose a modi- fication of the jackknife empirical likelihood to recover asymptotic pivotalness under both the conventional and nonstandard asymptotics. Our modification works for all above examples and provides a unified framework to investigate nonstandard asymptotic problems.

Item Type: Article
Official URL: https://academic.oup.com/biomet
Additional Information: © 2020 Biometrika Trust
Divisions: Economics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 04 Sep 2020 10:24
Last Modified: 20 Jul 2021 23:13
URI: http://eprints.lse.ac.uk/id/eprint/106488

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