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Covariates Hiding in the Tails

Milian Bachem, Lerby Ergun and Casper de Vries ()

Staff Working Papers from Bank of Canada

Abstract: Scaling behavior measured in cross-sectional studies through the tail index of a power law is prone to a bias. This hampers inference; in particular, time variation in estimated tail indices may be erroneous. In the case of a linear factor model, the factor biases the tail indices in the left and right tail in opposite directions. This fact can be exploited to reduce the bias. We show how this bias arises from the factor, how to remedy for the bias and how to apply our methods to financial data and geographic location data.

Keywords: Econometric; and; statistical; methods (search for similar items in EconPapers)
JEL-codes: C01 C14 C58 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2021-09
New Economics Papers: this item is included in nep-ecm and nep-ore
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Handle: RePEc:bca:bocawp:21-45