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Extreme Value Estimation for Heterogeneous Data

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  • John H. J. Einmahl
  • Yi He

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.

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  • John H. J. Einmahl & Yi He, 2022. "Extreme Value Estimation for Heterogeneous Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 255-269, December.
    • Handle: RePEc:taf:jnlbes:v:41:y:2022:i:1:p:255-269
    DOI: 10.1080/07350015.2021.2008408
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    Cited by:

    1. Einmahl, John & He, Y., 2022. "Extreme Value Inference for General Heterogeneous Data," Discussion Paper 2022-017, Tilburg University, Center for Economic Research.
    2. Einmahl, John & He, Y., 2022. "Extreme Value Inference for General Heterogeneous Data," Other publications TiSEM fd8dd91c-086f-40e6-ac29-3, Tilburg University, School of Economics and Management.

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