Bootstrap confidence intervals for the pareto index

Abstract

In the present paper we develop second-order theory using the subsample bootstrap in the context of Pareto index estimation. We show that the bootstrap is not second-order accurate, in the sense that it fails to correct the first term describing departure from the limit distribution. Worse than this, even when the subsample size is chosen optimally, the error between the subsample bootstrap approximation and the true distribution is often an order of magnitude larger than that oi tue asymptotic approximation. To overcome this deficiency, we show that an extrapolation method, based quite literally on a mixture of asymptotic and subsample bootstrap methods, can lead to second-order correct confidence intervals for the Pareto index.