Package 'changepointTests'

Pseudo-observations

Description

Pseudo-observations used in Nasri, Remillard, Bahraoui (2021). The values represent conditional cdfs of Gaussian HMM models applied to log-returns of Nasdaq and Dow Jones Industrial indexes from 2007 and 2008. If the models are correct, the pseudo-observations should be almost iid with uniform distribution.

Usage

data(pseudos)

Format

Pseudo-observations from Gaussian HMM models with 3 regimes for log-returns of the to Nasdaq index and Dow Jones Industrial indexes from 2007 and 2008.

• 1st column: pseudo-observations of a Gaussian HMM model with 3 regimes applied to the Nasdaq log-returns.

• 2nd column: pseudo-observations of a Gaussian HMM model with 3 regimes applied to the Dow Jones Industrial log-returns.

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Function to perform changepoint tests with multiplier bootstrap using the usual sequential process

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.

Usage

test.change.point(
  x,
  N = 1000,
  n_cores = 2,
  boot.method = "multipliers",
  est = FALSE
)

Arguments

x	(n x d) matrix of data (observations or pseudo-observations, including residuals), d>=1
N	number of multipliers samples to compute the P-value
n_cores	number of cores for parallel computing (default = 2)
boot.method	bootstrapping method: 'multipliers' (default, fastest) or 'bootstrap'
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.

Examples

x=matrix(rnorm(600),ncol=3)
out = test.change.point(x)

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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)

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