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Maximum Entropy Bootstrap Algorithm Enhancements

Author

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  • Hrishikesh D. Vinod

    (Fordham University)

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.

Suggested Citation

  • Hrishikesh D. Vinod, 2013. "Maximum Entropy Bootstrap Algorithm Enhancements," Fordham Economics Discussion Paper Series dp2013-04, Fordham University, Department of Economics.
  • Handle: RePEc:frd:wpaper:dp2013-04
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    File URL: https://archive.fordham.edu/ECONOMICS_RESEARCH/PAPERS/DP2013_04_Vinod.pdf
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    References listed on IDEAS

    as
    1. Vinod, Hrishikesh D., 2006. "Maximum entropy ensembles for time series inference in economics," Journal of Asian Economics, Elsevier, vol. 17(6), pages 955-978, December.
    2. Vinod, Hrishikesh D. & Lopez-de-Lacalle, Javier, 2009. "Maximum Entropy Bootstrap for Time Series: The meboot R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i05).
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    Keywords

    Maximum entropy; block bootstrap; variance; symmetry; R-software;

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