Description Usage Arguments Details Value Author(s) References Examples

Generate bootstrap samples for stationary data, using a
truncated geometric distribution to more accurately determine the value of the
`p`

parameter involved in the algorithm.

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`tseries` |
a numeric vector or time series giving the original data. |

`nb` |
the number of bootstrap series to compute. |

`b.info` |
if TRUE, the value of the |

The value of the `b`

parameter involved in the stationary bootstrap
algorithm is determined using the heuristic laid out in Politis & White (2004).
Then, the value of the `p`

parameter is found by numerically solving a
polynomial of order N+1 in variable `q`

, where *q = 1-p* and N is the
length of the data supplied.
The previous polynomial is derived using the expectation of a truncated geometric
distribution (for the stochastic block length), shown in Olatayo (2014).

The general structure of the algorithm is similar to the one laid out in James & Yang (2010).

If b.info is FALSE, a matrix or time series with nb columns and length(tseries) rows
containing the bootstrap data. Each column contains one bootstrap sample.
If b.info is TRUE, a list with two fields: one containing the bootstrap data, and
another containing the `b`

value found.

Albert Dorador

Politis, D.N. and Romano, J.P. (1994), 'The stationary bootstrap', Journal of the American Statistical Association 89(428), 1303-1313.

Politis, D.N. and White, H. (2004), 'Automatic block-length selection for the dependent bootstrap', Econometric Reviews 23(1), 53-70.

Olatayo, T.O. (2014), 'Truncated geometric bootstrap method for timeseries stationary process', Applied Mathematics 5, 2057-2061.

James, J. and Yang, L. (2010), 'Stop-losses, maximum drawdown-at-risk and replicating financial time series with the stationary bootstrap', Quantitative Finance 10(1), 1-12.

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