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.
Original language | English |
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Pages (from-to) | 255-269 |
Number of pages | 15 |
Journal | Journal of Business & Economic Statistics |
Volume | 41 |
Issue number | 1 |
DOIs | |
Publication status | Published - Jan 2023 |
Keywords
- power law
- extreme values
- heterogeneous data
- COVD-19
- inequality
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Extreme Value Estimation for Heterogeneous Data
Einmahl, J. H. J. (Creator) & He, Y. (Creator), Taylor & Francis, 3 Dec 2021
DOI: 10.6084/m9.figshare.17124050.v1, https://tandf.figshare.com/articles/dataset/Extreme_Value_Estimation_for_Heterogeneous_Data/17124050/1
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