The extremal index $\theta $, a number in the interval $[0,1]$, is known to be a measure of primal importance for analyzing the extremes of a stationary time series. New rank-based estimators for $\theta $ are proposed which rely on the construction of approximate samples from the exponential distribution with parameter $\theta $ that is then to be fitted via the method of moments. The new estimators are analyzed both theoretically as well as empirically through a large-scale simulation study. In specific scenarios, in particular for time series models with $\theta \approx 1$, they are found to be superior to recent competitors from the literature.
"Method of moments estimators for the extremal index of a stationary time series." Electron. J. Statist. 14 (2) 3103 - 3156, 2020. https://doi.org/10.1214/20-EJS1734