Tail Index Regression
28 Pages Posted: 11 Feb 2009
Date Written: February 10, 2009
In extreme value statistics, the tail index is an important measure to gauge the heavy-tailed behavior of a distribution. Under Pareto-type distributions, we employ the logarithmic function to link the tail index to the linear predictor induced by covariates, which constitutes the tail index regression model. We then propose an approximate log-likelihood function to obtain regression parameter estimators, and subsequently show the asymptotic normality of those estimators. Numerical studies are presented to illustrate theoretical findings.
Keywords: Generalized Extreme Distribution; Generalized Pareto Distribution; Hill Estimator; Pareto-type Distribution; Tail Index Regression
JEL Classification: C10, C13
Suggested Citation: Suggested Citation