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Lorenzo Camponovo, Lugano Print
Thursday, 29 April 2010, 12:15 - 13:15

Robust Resampling Methods for Time Series

Lorenzo Camponovo, University of Lugano

Abstract: We study the robustness of block resampling procedures for time series. We first derive a set of formulas to quantify their quantile breakdown point. For the block bootstrap and the subsampling, we find a very low quantile breakdown point. A similar robustness problem arises in relation to data-driven methods for selecting the block size in applications, which can render inferences based on standard resampling methods useless already in simple estimation and testing settings. To solve this problem, we introduce a robust fast resampling scheme that is applicable to a wide class of time series settings. Monte Carlo simulations and sensitivity analysis for the simple AR(1) model confirm the dramatic fragility of classical resampling procedures in presence of contaminations by outliers. They also show the better accuracy and efficiency of the robust resampling approach under different types of data constellations. A real data application to testing for stock returns predictability shows that our robust approach can detect predictability structures more consistently than classical methods.

Location: S 12.227
Contact: Claude Adan, This e-mail address is being protected from spam bots, you need JavaScript enabled to view it

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