Description Usage Arguments Details References Examples
View source: R/McCloskey_Perron.R
McC.Perron
trimmed and adaptive log-periodogram estimators of
McCloskey and Perron (2013, ET) for robust estimation of the memory parameter d.
1 2 | McC.Perron(X, m, epsilon = 0.05, method = c("adaptive", "trimmed"),
Kl = 1)
|
X |
vector of length T. |
m |
bandwith parameter specifying the number of Fourier frequencies.
used for the estimation usually |
epsilon |
small constant that determines the choice of the trimming parameter l
used by the |
method |
either "adaptive" or "trimmed" for the corresponding estimator.
Confer McCloskey and Perron (2013, ET) for details. Default is |
Kl |
proportionality factor for bandwidth selection. Default is |
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.
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.
1 2 3 4 5 6 | 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)
|
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