Robust Resampling Methods for Time Series

We study the robustness of block resampling procedures for time series. We first derive a setof formulas to quantify their quantile breakdown point. For the block bootstrap and the sub-sampling, we find a very low quantile breakdown point. A similar robustness problem arisesin relation to data-driven methods for selecting the block size in applications, which can ren-der inferences based on standard resampling methods useless already in simple estimation andtesting settings. To solve this problem, we introduce a robust fast resampling scheme that isapplicable to a wide class of time series settings.[...]