CONSISTENCY OF HILL ESTIMATOR FOR DEPENDENT DATA

Consider a sequence of possibly dependent random variables having the same marginal distribution F, whose tail 1-F is regularly varying at infinity with an unknown index - alpha 0 which is to be estimated. For i.i.d. data or for dependent sequences with the same marginal satisfying mixing conditions, it is well known that Hill's estimator is consistent for alpha(-1) and asymptotically normally distributed. The purpose of this paper is to emphasize the central role played by the tail empirical process for the problem of consistency. This approach allows us to easily prove Hill's estimator is consistent for infinite order moving averages of independent random variables. Our method also suffices to prove that, for the case of an AR model, the unknown index can be estimated using the residuals generated by the estimation of the autoregressive parameters.