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edited Apr 13, 2017 at 12:44

As pointed out by the other poster, you cannot treat time series data as a simple random sample due to the correlations between adjacent observations in time. A nice nonparametric approach to generating sample paths is the block boostrap block boostrap and here http://nccur.lib.nccu.edu.tw/bitstream/140.119/35143/6/51007106.pdf

Note that the first link also points you to the handy tsboot package in R.

As pointed out by the other poster, you cannot treat time series data as a simple random sample due to the correlations between adjacent observations in time. A nice nonparametric approach to generating sample paths is the block boostrap and here http://nccur.lib.nccu.edu.tw/bitstream/140.119/35143/6/51007106.pdf

Note that the first link also points you to the handy tsboot package in R.

As pointed out by the other poster, you cannot treat time series data as a simple random sample due to the correlations between adjacent observations in time. A nice nonparametric approach to generating sample paths is the block boostrap and here http://nccur.lib.nccu.edu.tw/bitstream/140.119/35143/6/51007106.pdf

Note that the first link also points you to the handy tsboot package in R.

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created Jun 7, 2016 at 4:00

user75138

As pointed out by the other poster, you cannot treat time series data as a simple random sample due to the correlations between adjacent observations in time. A nice nonparametric approach to generating sample paths is the block boostrap and here http://nccur.lib.nccu.edu.tw/bitstream/140.119/35143/6/51007106.pdf

Note that the first link also points you to the handy tsboot package in R.