partition.X {LongMemoryTS}

Automated partitioning of estimated vector of long memory parameters into subvectors with equal memory.

Description

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).

Usage

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

Arguments

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.

Details

add a lot of details.

References

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.

See Also

partitions, T.rho, T0stat

Examples

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)