test.change.point.copula.BKRS {changepointTests}

Function toperform changepoint test for the copula with multiplier bootstrap using for changepoint the new sequential process of Bucher et al (2014)

Description

This function compute the Cramer-von Mises and Kolmogorov-Smirnov test statistics based on the new sequential process of Bucher et al (2014), using multipliers and parallel computing. Two methods of bootstrapping are used: non-sequential (fastest) and sequential. Both methods yields basically the same P-valueas.

Usage

test.change.point.copula.BKRS(
  x,
  N = 1000,
  n_cores = 2,
  method = "nonseq",
  est = FALSE
)

Arguments

x

(n x d) matrix of data (observations or pseudo-observations, including residuals), d >=2

N

number of multipliers samples to compute the P-value

n_cores

number of cores for parallel computing (default = 2)

method

'nonseq' (default) or 'seq'

est

if TRUE, tau is estimated (default = FALSE)

Value

CVM

Cramer-von Mises statistic

KS

Kolmogorov-Smirnov statistic

pvalueCVM

Pvalue for the Cramer-von Mises statistic

pvalueKS

Pvalue for theKolmogorov-Smirnov statistic

tauCVM

Estimated changepoint using the Cramer-von Mises statistic

tauKS

Estimated changepoint using the Kolmogorov-Smirnov statistic

Author(s)

Bouchra R Nasri and Bruno N Remillard, August 6, 2020

References

Nasri, B. R. Remillard, B., & Bahraoui, T. (2022). Change-point problems for multivariate time series using pseudo-observations, J. Multivariate Anal., 187, 104857.

Bucher, A., Kojadinovic, I., Rohmer, T., & Segers, J. (2014). Detecting changes in cross-sectional dependence in multivariate time series, J. Multiv. Anal., 132, 111–128.

Examples

x<-matrix(rnorm(100),ncol=2)
out = test.change.point.copula.BKRS(x)