Cookies?
Library Header Image
LSE Research Online LSE Library Services

Estimating conditional means with heavy tails

Peng, Liang and Yao, Qiwei (2017) Estimating conditional means with heavy tails. Statistics and Probability Letters, 127. pp. 14-22. ISSN 0167-7152

[img]
Preview
Text - Accepted Version
Download (371kB) | Preview

Identification Number: 10.1016/j.spl.2017.03.023

Abstract

When a conditional distribution has an infinite variance, commonly employed kernel smoothing methods such as local polynomial estimators for the conditional mean admit non-normal limiting distributions (Hall et al., 2002). This complicates the related inference as the conventional tests and confidence intervals based on asymptotic normality are no longer applicable, and the standard bootstrap method often fails. By utilizing the middle part of data nonparametrically and the tail parts parametrically based on extreme value theory, this paper proposes a new estimation method for conditional means, resulting in asymptotically normal estimators even when the conditional distribution has infinite variance. Consequently the standard bootstrap method could be employed to construct, for example, confidence intervals regardless of the tail heaviness. The same idea can be applied to estimating the difference between a conditional mean and a conditional median, which is a useful measure in data exploratory analysis.

Item Type: Article
Official URL: http://www.elsevier.com/locate/issn/01677152
Additional Information: © 2017 Elsevier B.V.
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Sets: Departments > Statistics
Date Deposited: 07 Apr 2017 16:50
Last Modified: 20 Mar 2019 03:13
URI: http://eprints.lse.ac.uk/id/eprint/73082

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics