diffseries: Fractionally differenced series for any value of d

In TSF: Two Stage Forecasting (TSF) for Long Memory Time Series in Presence of Structural Break

Description

The function fdseries computes the fractional differenced series for any value of d i.e. positive or negetive.

Usage

fdseries(x, d)

Arguments

x	univariate time series
d	The orer of fractional differencing to be done

Value

fdseries

fractionally differenced series for both positive as well as negetive d

Author(s)

Sandipan Samanta, Ranjit Kumar Paul and Dipankar Mitra

References

Papailias, F. and Dias, G. F. 2015. Forecasting long memory series subject to structural change: A two-stage approach. International Journal of Forecasting, 31, 1056 to 1066.

Wang, C. S. H., Bauwens, L. and Hsiao, C. 2013. Forecasting a long memory process subject to structural breaks. Journal of Econometrics, 177, 171-184.

Reisen, V. A. (1994) Estimation of the fractional difference parameter in the ARFIMA(p,d,q) model using the smoothed periodogram. Journal Time Series Analysis, 15(1), 335 to 350.

Examples

## Simulating Long Memory Series
N <- 1000
PHI <- 0.2
THETA <- 0.1
SD <- 1
M <- 0
D <- 0.2
Seed <- 123

set.seed(Seed)
Sim.Series <- fracdiff::fracdiff.sim(n = N, ar = c(PHI), ma = c(THETA),
d = D, rand.gen = rnorm, sd = SD, mu = M)

Xt <- as.ts(Sim.Series$series)

## fractional differencing
fdseries(Xt,d=D)

TSF documentation built on May 2, 2019, 6:34 a.m.