Maximum Entropy Bootstrap Algorithm Enhancements
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.
|Date of creation:||2013|
|Date of revision:|
|Contact details of provider:|| Web page: http://www.fordham.edu/economics/|
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Hrishikesh D. Vinod & Javier Lopez-de-Lacalle, . "Maximum Entropy Bootstrap for Time Series: The meboot R Package," Journal of Statistical Software, American Statistical Association, vol. 29(i05).
- 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.
When requesting a correction, please mention this item's handle: RePEc:frd:wpaper:dp2013-04. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Fordham Economics)
If references are entirely missing, you can add them using this form.