Maximum Entropy Bootstrap Algorithm Enhancements

Hrishikesh Vinod

Fordham Economics Discussion Paper Series from Fordham University, Department of Economics

Abstract: While moving block bootstrap (MBB) has been used for mildly dependent (m-dependent) time series, maximum entropy (ME) bootstrap (meboot) is perhaps the only tool for inference involving perfectly dependent, nonstationary time series, possibly subject to jumps, regime changes and gaps. This brief note describes the logic and provides the R code for two potential enhancements to the meboot algorithm in Vinod and Lopez-de-Lacalle (2009), available as the 'meboot' package of the R software. The first 'rescaling enhancement' adjusts the of meboot resampled elements so that the population variance of the ME density equals that of the original data. Our second 'symmetrizing enhancement' forces the ME density to be symmetric. One simulation involving inference for regression standard errors suggests that the symmetrizing enhancement of the meboot continues to outperform the MBB.

Keywords: Maximum entropy; block bootstrap; variance; symmetry; R-software (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (3)

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