b.star {boodd}R Documentation

Bootstrap Block Length Choice in the Stationary Case

Description

This function calculates the optimal bootstrap block lengths for both the Stationary Bootstrap, Nonoverlapping Block Bootstrap, Circular Block Bootstrap and Moving Block Bootstrap methods, based on the data provided.

Usage

b.star(data, mmax = NULL, Bmax = NULL, round = FALSE)

Arguments

data

A time series or a matrix of time series data.

mmax

An integer constant representing the maximum lag. By default determined by the data.

Bmax

An integer constant representing the maximum block length By default determined by the data.

round

A boolean indicating whether the resulting block lengths should be rounded.

Details

A b.star computes optimal block lengths for bootstrapping time series data, utilizing autocorrelation and autocovariance measures. The function incorporates several parameters, including mmax, and Bmax, to refine the block length calculations. The method involves a detailed analysis of the data's autocorrelation structure to identify the most suitable block lengths for bootstrapping procedures.

Value

A 2 x k matrix, where each column represents a time series and each row provides the optimal block length for the Moving Block Bootstrap or Circular Block Bootstrap (first row) and the Nonoverlapping Block Bootstrap or Stationary Bootstrap (second row).

Author(s)

Original code in Matlab by A. Patton. R translation and modifications by C. Parmeter and J. Racine, racinej@mcmaster.ca. We are grateful to Andrew Patton and Dimitris Politis for their assistance and feedback, and for allowing us to include this function in this package.

References

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

Patton, A., Politis, D.N., and White, H. (2009). Correction to 'Automatic Block-Length Selection for the Dependent Bootstrap' by D.N. Politis and H. White." Econometric Reviews, 28, 372-375.

See Also

blockboot, lam.

Examples

# Simulate an ARIMA process
X <- arima.sim(n = 200, model = list(ar = c(0.5, 0.4), na = 0.5))
# Calculate the optimal bootstrap block lengths
optimal_choice <- b.star(X)
print(optimal_choice)

[Package boodd version 0.1 Index]