per_boo {boodd}R Documentation

Bootstrap of Periodogram

Description

Computes periodogram values at Fourier frequencies for a time series, smooths the periodogram to estimate the spectral density, and generates the bootstrap version of the periodogram.

Usage

per_boo(x, B, taper0 = 0)

Arguments

x

A vector or a time series.

B

A positive integer; the number of bootstrap replications.

taper0

A numeric value; specifies the proportion of data to taper. The default value is 0, that is there is no tapering.

Details

The function first centers the input time series and calculates the values of the periodogram at Fourier frequencies using spec.pgram. Spectral density is then estimated by applying a kernel smoother to the periodogram values, with the smoothing bandwidth computed as sd(x) * n^(-1/3). Bootstrap is then performed by resampling periodogram ordinates.

The function outputs a graph of the histogram of the periodogram ordinates which should be close to an exponential density.

Value

A list containing:

References

Bertail, P. and Dudek, A. (2025). Bootstrap for Dependent Data, with an R package (by Bernard Desgraupes and Karolina Marek) - submitted..

Bertail, P. and Dudek, A.E. (2021). Consistency of the Frequency Domain Bootstrap for differentiable functionals, Electron. J. Statist., 15, 1-36.

Hurvich, C. M. and Zeger, S. L. (1987). Frequency domain bootstrap methods for time series, Technical Report 87-115, Graduate School of Business Administration, New York Univ.

Lahiri, S.N. (2003). Resampling Methods for Dependent Data. Springer, New York.

See Also

tft_boot, func_fdb, freqboot.

Examples

set.seed(12345)
x=arima.sim(model=list(ar=0.8),n=200)
boo1=per_boo(x,99)

fn=length(boo1[[2]])
spec.pgram(x, plot=TRUE)
# Superimposed plots of 99 bootstrap periodograms
for ( i in (1:99)) {
lines(boo1[[2]],t(boo1[[1]]$s)[,i], type="l", col=i)
}

[Package boodd version 0.1 Index]