Extreme value estimation for heterogeneous data
Einmahl,John He,Y.
Einmahl,John
He,Y.
Abstract
We develop a universal econometric formulation of empirical power laws possibly driven by parameter heterogeneity. Our approach extends classical extreme value theory to specifying the tail behavior of the empirical distribution of a general dataset with possibly heterogeneous marginal distributions. We discuss several model examples that satisfy our conditions and demonstrate in simulations how heterogeneity may generate empirical power laws. We observe a cross-sectional power law for the U.S. stock losses and show that this tail behavior is largely driven by the heterogeneous volatilities of the individual assets.
Description
Publisher Copyright: © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.
Date
2023-01
Journal Title
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Volume Title
Publisher
Research Projects
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Journal Issue
Keywords
power law, extreme values, heterogeneous data, COVD-19, inequality, C14 - Semiparametric and Nonparametric Methods: General, C21 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions, I19 - Other, SDG 10 - Reduced Inequalities
Citation
Einmahl, J & He, Y 2023, 'Extreme value estimation for heterogeneous data', Journal of Business & Economic Statistics, vol. 41, no. 1, pp. 255-269. https://doi.org/10.1080/07350015.2021.2008408