
We discuss in detail the asymptotic distribution of sample expectiles. First, we show uniform consistency under the assumption of a finite mean. In case of a finite second moment, we show that for expectiles other then the mean, only the additional assumption of continuity of the distribution function at the expectile implies asymptotic normality, otherwise, the limit is non-normal. For a continuous distribution function we show the uniform central limit theorem for the expectile process. If, in contrast, the distribution is heavy-tailed, and contained in the domain of attraction of a stable law with $1 < ��< 2$, then we show that the expectile is also asymptotically stable distributed. Our findings are illustrated in a simulation section.
ddc:510, convergence to stable distributions, FOS: Computer and information sciences, asymptotic normality, M-estimator, 510, Methodology (stat.ME), uniform central limit theorem, expectiles, Mathematics, info:eu-repo/classification/ddc/510, Statistics - Methodology
ddc:510, convergence to stable distributions, FOS: Computer and information sciences, asymptotic normality, M-estimator, 510, Methodology (stat.ME), uniform central limit theorem, expectiles, Mathematics, info:eu-repo/classification/ddc/510, Statistics - Methodology
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