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I currently use the following process for bootstrapping a multivariate time series in R:

  1. Determine block sizes - run the function b.star in the np package which produces a block size for each series
  2. Select maximum block size
  3. Run tsboot on any series using the selected block size
  4. Use index from bootstrap output to reconstruct multivariate time series

Someone suggested using the meboot package as an alternative to the block bootstrap but since I am not using the entire data set to select a block size, I am unsure of how to preserve correlations between series if I were to use the index created by running meboot on one series. If anyone has experience with meboot in a multivariate setting, I would greatly appreciate advice on the process.

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  • $\begingroup$ Have you tried Sieve Bootstrap? I guess the question is about running multi-variate bootstrap while preserving dependence of each asset - and if using the block bootstrap on multivariate there is a good way to select block size for all? $\endgroup$
    – user5967
    Aug 22, 2013 at 14:21

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