modernBoot (version 0.1.1)

moving_block_boot: Moving Block Bootstrap for Time Series

Description

Performs moving block bootstrap resampling for dependent data (time series). Preserves temporal dependence structure within blocks.

Usage

moving_block_boot(x, block_size = 10, R = 1000)

Value

A list of length R. Each element is a numeric vector of length n (same as original series length), representing one bootstrap replicate of the time series. Replicates preserve block structure and local dependence within blocks, though global autocorrelation structure may be altered.

Arguments

x

numeric vector or time series object. Should be univariate time series with temporal dependence (autocorrelation). If using ts object, inherits frequency information. Length n >= block_size required.

block_size

integer length of consecutive observations to keep together in bootstrap samples (default 10). Rule of thumb: approximately sqrt(n) where n is series length. Must be >= 1 and <= length(x). Larger blocks preserve longer-range dependence; smaller blocks reduce distortion but may not capture autocorrelation structure.

R

integer number of bootstrap replicates (default 1000). Each replicate is a complete time series of length n obtained by concatenating randomly selected blocks. Must be >= 1.

Details

The moving block bootstrap divides the time series into overlapping blocks of length block_size and resamples these blocks with replacement. This preserves short-range dependence while allowing the empirical sampling distribution to reflect dependence.

Examples

set.seed(42)
x <- arima.sim(n = 100, list(ar = 0.7))
result <- moving_block_boot(x, block_size = 10, R = 100)
length(result)  # 100 bootstrap replicates