A New Regression-Based Tail Index Estimator

• 成果类型: Article
• 署名作者: Nicolau, Joao; Rodrigues, Paulo M. M.
• 署名单位: Universidade de Lisboa; Universidade de Lisboa; Banco de Portugal; Universidade Nova de Lisboa
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_00768
• 发表日期: 2019-10
• 页码: 667-680
• 关键词: LAW; exponent; cities

摘要

A new regression-based approach for the estimation of the tail index of heavy-tailed distributions with several important properties is introduced. First, it provides a bias reduction when compared to available regression-based methods; second, it is resilient to the choice of the tail length used for the estimation of the tail index; third, when the effect of the slowly varying function at infinity of the Pareto distribution vanishes slowly, it continues to perform satisfactorily; and fourth, it performs well under dependence of unknown form. An approach to compute the asymptotic variance under time dependence and conditional heteroskcedasticity is also provided.