A moving window approach for nonparametric estimation of the conditional tail index

Details A moving window approach for nonparametric estimation of the conditional tail index A moving window approach for nonparametric estimation of the conditional tail index

2011-04-05

arxiv.org/abs/1104.0763v1

Journal of Multivariate Analysis, 99, 2368-2388 (2008)

Mathematics

Statistics Theory

Scientific paper

We present a nonparametric family of estimators for the tail index of a Pareto-type distribution when covariate information is available. Our estimators are based on a weighted sum of the log-spacings between some selected observations. This selection is achieved through a moving window approach on the covariate domain and a random threshold on the variable of interest. Asymptotic normality is proved under mild regularity conditions and illustrated for some weight functions. Finite sample performances are presented on a real data study.

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