StructuralBrek: Predicting fractionally differenced series in presence of...

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

Description

The function is used for prediction of long memory time series in presence of structural break

Usage

StructuralBrekwithLongmemory(ts,bandwidth)

Arguments

ts	univariate time series
bandwidth	the bandwidth used in the regression equation

Value

StructuralBrekwithLongmemory

the updated series at first step of TSF appraoch, prediction based on TSF approach and the estimate of long memory parameter

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
bandwidth<-0.9
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)

## Forecasting using TSF method
StructuralBrekwithLongmemory(Xt,bandwidth)

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