Cookies?
Library Header Image
LSE Research Online LSE Library Services

Local M-estimation with discontinuous criterion for dependent and limited observations

Seo, Myung Hwan and Otsu, Taisuke (2018) Local M-estimation with discontinuous criterion for dependent and limited observations. Annals of Statistics, 46 (1). pp. 344-369. ISSN 0090-5364

[img]
Preview
Text - Accepted Version
Download (546kB) | Preview
Identification Number: 10.1214/17-AOS1552

Abstract

This paper examines asymptotic properties of local M-estimators under three sets of high-level conditions. These conditions are sufficiently general to cover the minimum volume predictive region, conditional maximum score estimator for a panel data discrete choice model, and many other widely used estimators in statistics and econometrics. Specifically, they allow for discontinuous criterion functions of weakly dependent observations, which may be localized by kernel smoothing and contain nuisance parameters whose dimension may grow to infinity. Furthermore, the localization can occur around parameter values rather than around a fixed point and the observation may take limited values, which leads to set estimators. Our theory produces three different nonparametric cube root rates and enables valid inference for the local M-estimators, building on novel maximal inequalities for weakly dependent data. Our results include the standard cube root asymptotics as a special case. To illustrate the usefulness of our results, we verify our conditions for various examples such as the Hough transform estimator with diminishing bandwidth, maximum score-type set estimator, and many others.

Item Type: Article
Official URL: http://www.imstat.org/aos/
Additional Information: © 2017 Institute of Mathematical Statistics
Divisions: Economics
Subjects: H Social Sciences > H Social Sciences (General)
Q Science > QA Mathematics
Sets: Departments > Economics
Date Deposited: 29 Nov 2017 13:51
Last Modified: 20 Apr 2019 00:04
Projects: SNP 615882
Funders: Seoul National University, ERC Consolidator Grant
URI: http://eprints.lse.ac.uk/id/eprint/85872

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics