A block resampling method used for weakly-dependent stationary time-series data#
A block resampling method used for weakly-dependent stationary time-series data proposed in the 1994 paper by Politis & Romano.
When using non-parametric tools to generate counterfactual scenarios or empirical distributions, bootstrapping methods proved to be a powerful and easy-to-use tools. However, the bootstrap in its simplest implementation assumes a time-series in which observations are independent. In a lot of applications this is not the case.
An example of this is interest rate modelling when business cycles need to be considered. The presence of business cycles makes the time-series weakly time dependent. To account for this, block-resampling techniques are used.
Stationary bootstrap is a block-resampling technique that relaxes the assumption of a fixed length of a sampling block. The user still needs to specify an average length, but because this is true only on average, shorter/longer blocks are also present in the final sample. The algorithm works by randomly selecting a starting point in the time-series and at each step it either increases the block size by one or selects a new block with a new starting point. This choice happens with a fixed probability governed by the parametrisation.
A time-series that you want to bootstrap
The parameter m describing the average duration of the blocks in the sample
The length of the output sample
Vector of bootstrapped values of specified length
Given the time-series with observed values 0.4, 0.2, 0.1, 0.4, 0.3, 0.1, 0.3, 0.4, 0.2, 0.5, 0.1, and 0.2, the user is looking to bootstrap a new sample of length 9 where the average block is of size 4.
import numpy as np from StationaryBootstrap import StationaryBootstrap # Original time-series data = np.array([0.4,0.2,0.1,0.4,0.3,0.1,0.3,0.4,0.2,0.5,0.1,0.2]) # Average length of the block m = 4 # Length of output sample sampleLength = 12 ans = StationaryBootstrap(data, m, sampleLength) print(ans)