partition.X: Automated partitioning of estimated vector of long memory...
In LongMemoryTS: Long Memory Time Series

Description Usage Arguments Details References See Also Examples

partition.X conducts a sequence of tests for the equality of two or more estimated memory parameters to find possible partitions of a vector into subvectors with equal memory parameters. The procedure follows Robinson and Yajima (2002).

1	partition.X(data, d.hat, m, m1, alpha = 0.05, report = FALSE)

`data`	(Txq) data matrix
`d.hat`	(qx1) vector of d-estimates obtained using a local Whittle method such as that described in Robinson (1995).
`m`	the bandwidth parameter to be used for estimation of G
`m1`	the bandwidth parameter used for estimation of d.vec with m1>>m
`alpha`	the desired significance level for the tests
`report`	either `TRUE` or `FALSE` determining, whether information about the partitioning process should be printed to the user. Default is `report=FALSE`.

add a lot of details.

Robinson, P. M. (1995): Gaussian semiparametric estimation of long rang dependence. The Annals of Statistics, Vol. 23, No. 5, pp. 1630-1661.

Robinson, P. M. and Yajima, Y. (2002): Determination of cointegrating rank in fractional systems. Journal of Econometrics, Vol. 106, No.2, pp. 217-241.

partitions, T.rho, T0stat

library(fracdiff)
T<-1000
d1<-0.2
d2<-0.4
X<-cbind(fracdiff.sim(n=T,d=d1)$series,fracdiff.sim(n=T,d=d1)$series,
fracdiff.sim(n=T,d=d2)$series,fracdiff.sim(n=T,d=d2)$series)
alpha<-0.05
m1<-floor(1+T^0.75)
m<-floor(1+T^0.65)
d.hat<-c(local.W(X[,1],m=m1)$d,local.W(X[,2],m=m1)$d,local.W(X[,3],m=m1)$d,local.W(X[,4],m=m1)$d)
partition.X(data=X, d.hat=d.hat, m=m, m1=m1, alpha=0.05, report=TRUE)