McC.Perron {LongMemoryTS}

GPH estimation of long memory parameter robust to low frequency contaminations.

Description

McC.Perron trimmed and adaptive log-periodogram estimators of McCloskey and Perron (2013, ET) for robust estimation of the memory parameter d.

Usage

McC.Perron(X, m, epsilon = 0.05, method = c("adaptive", "trimmed"),
  Kl = 1)

Arguments

X

vector of length T.

m

bandwith parameter specifying the number of Fourier frequencies. used for the estimation usually floor(1+T^delta), where 0<delta<1.

epsilon

small constant that determines the choice of the trimming parameter l used by the gph estimator. Default is epsilon=0.05.

method

either "adaptive" or "trimmed" for the corresponding estimator. Confer McCloskey and Perron (2013, ET) for details. Default is method="adaptive".

Kl

proportionality factor for bandwidth selection. Default is Kl=1.

Details

add details here. Recommendation of McCloskey, A. and Perron, P. (2013): Use trimmed version of estimator if there is reason to assume that shifts are present and use adaptive with epsilon=0.05 and m=T^0.8 if you are agnostic about the presence of shifts.

References

Robinson, P. M. (1995): Log-periodogram regression of time series with long range dependence. The Annals of Statistics, Vol. 23, No. 5, pp. 1048 - 1072.

McCloskey, A. and Perron, P. (2013): Memory parameter estimation in the presence of level shifts and deterministic trends. Econometric Theory, 29, pp. 1196-1237.

Examples

library(fracdiff)
T<-1000
m<-floor(1+T^0.8)
d=0.4
series<-fracdiff.sim(n=T, d=d)$series
McC.Perron(series,m)