Extreme value estimation for heterogeneous data

Research output: Contribution to journalArticleScientificpeer-review

Abstract

We develop a universal econometric formulation of the empirical power laws possibly driven by parameter heterogeneity. Our approach extends classical extreme value theory to specifying the behavior of the empirical distribution of a general data set with possibly heterogeneous marginal distributions and a complex dependence structure. The main assumption is that in the intermediate tail the empirical distribution approaches some heavy-tailed distribution with a positive extreme value index. In this setup the Hill estimator consistently estimates this extreme value index and, on a log-scale, extreme quantiles are consistently estimated. We discuss several model examples that satisfy our conditions and demonstrate in simulations how heterogeneity may generate the dynamics of empirical power laws. We observe a dynamic cross-sectional power law for the new confirmed COVID-19 cases and deaths per million people across countries, and show that this international inequality is largely driven by the heterogeneity of the countries' scale parameters.
Original languageEnglish
JournalJournal of Business & Economic Statistics
DOIs
Publication statusE-pub ahead of print - Dec 2021

Keywords

  • power law
  • extreme values
  • heterogeneous data
  • COVD-19
  • inequality

Fingerprint

Dive into the research topics of 'Extreme value estimation for heterogeneous data'. Together they form a unique fingerprint.

Cite this