Covariates hiding in the tails
研究发现，通过幂律尾部指数衡量的横截面研究存在偏差，这种偏差源于线性因子模型中的因子，并提出了减少偏差的方法，适用于金融和地理数据。
Summary: Scaling behaviour measured in cross-sectional studies through the tail index of a power law is prone to a bias. This hampers inference; in particular, observed time variation in estimated tail indices may not originate from the data-generating process. In the case of a linear factor model, the factors bias 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 factors, how to remedy for the bias, and how to apply our methods to financial data and geographic location data.