MLWS: MLWS test for multivariate spurious long memory.

Description Usage Arguments Details References Examples

View source: R/MLWS.R

Description

Multivariate local Whittle Score type test for the null hypothesis of true long memory against the alternative of spurious long memory suggested by Sibbertsen, Leschinski and Holzhausen (2015).

Usage

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MLWS(X, m, epsilon = c(0.02, 0.05), coint.elements = NULL, B = NULL,
  prewhite = c("none", "uni", "multi"), eta = rep(1/sqrt(min(dim(X))),
  min(dim(X))), rep = FALSE, approx = 100, split = 1, T_limdist = 1000,
  M_limdist = 5000)

Arguments

X

data matrix

m

bandwith parameter specifying the number of Fourier frequencies used for the estimation usually floor(1+T^delta), where 0.5<delta<0.8 for consistency.

epsilon

trimming parameter epsilon=0.05 by default. Determines minimum number of Fourier frequencies used in test statistic. For T>500 it is recommended to use epsilon=0.02. Confer Sibbertsen, Leschinski, Holzhausen (2015) for further details.

coint.elements

Vector specifying which elements in the vector series are in a cointegrating relationship. By default NULL. Cf details.

B

cointegrating matrix, if known. Default is B=NULL.

prewhite

specifies the form of pre-whitening applied. One of c("none","uni","multi"). If uni is selected the univariate a univariate of maximal order (1,d,1) is selected using the AIC. If multi is selected VARFIMA_est is used to fit a VARFIMA(1,d,1) in final equations form. Default is none.

eta

vector of weights. Default is rep(1/sqrt(min(dim(X))),min(dim(X))).

rep

if prewhite="multi" is selected, rep specifies whether the current parameter values are displayed to the user during optimization procedure. Default is rep=FALSE.

approx

if prewhite="multi" is selected, approx specifies the order of the AR-approximation used in VARFIMA_est. Default is approx=100.

split

if prewhite="multi" is selected, split whether the sample should be split into subsamples to speed up the estimation. Default is split=1, so that the whole sample is used.

T_limdist

number of increments used in simulation if limit distribution. Only relevant for component-wise version of the test. Default is T_limdist=1000.

M_limdist

number of replications for simulation of the limit distribution. Default is M_limdist=5000.

Details

add details here

References

Sibbertsen, P., Leschinski, C. H., Holzhausen, M., (2015): A Multivariate Test Against Spurious Long Memory. Hannover Economic Paper.

Examples

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T<-500
m<-floor(1+T^0.75)
series<-FI.sim(T=T,q=2,rho=0.7,d=c(0.4,0.2))
ts.plot(series, col=1:2)
MLWS(X=series, m=m, epsilon=0.05)

shift.series<-series+ARRLS.sim(T=T, phi=0, sig.shift=2, prob=5/T)
ts.plot(shift.series, col=1:2)
MLWS(X=shift.series, m=m, epsilon=0.05)

T<-500
m<-floor(T^0.75)
series<-FI.sim(T=T,q=2,rho=0,d=c(0.1,0.4), B=rbind(c(1,-1),c(0,1)))
ts.plot(series, col=1:2)
MLWS(series, m=m)
MLWS(series, m=m, coint.elements=c(1,2))

FunWithR/LongMemoryTS documentation built on June 9, 2018, 12:22 a.m.