Résumés
Abstract
In this paper, we propose a novel entropy-based resampling scheme valid for non-stationary data. In particular, we identify the reason for the failure of the original entropy-based algorithm of Vinod and López-de Lacalle (2009) to be the perfect rank correlation between the actual and bootstrapped time series. We propose the Maximum Entropy Block Bootstrap which preserves the rank correlation locally. Further, we also introduce the Maximum non-extensive Entropy Block Bootstrap to allow for fat tail behaviour in time series. Finally, we show the optimal finite sample properties of the proposed methods via a Monte Carlo analysis where we bootstrap the distribution of the Dickey-Fuller test.
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Parties annexes
Acknowledgements
We wish to thank participants in the 15th OxMetrics Conference (Cass Business School, 4-5 September, 2014), in particular to Guillaume Chevillon for useful comments. Special thanks to Russell Davidson and Lynda Khalaf for very useful comments on a previous version of the paper. We are greatly in debt with the Editor, Marie-Claude Beaulieu, and an anonymous referee for providing us with very insightful comments and suggestions that greatly helped to improve the paper. The usual disclaimer applies. Michele Bergamelli acknowledges financial support from the “PhD Scholarship in Memory of Ana Timberlake”, while Jan Novotny acknowledges financial support from the Centre for Econometric Analysis and GA ˇCR grant 14-27047S.
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