forecastTSF: Forecasting fractionally differenced series using TSF...

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

Description

The function is used for forecasting long memory time series using TSF approach

Usage

forecastTSF(N0,Xt,bandwidth)

Arguments

N0	lead period of forecast
Xt	univariate time series
bandwidth	the bandwidth used in the regression equation

Value

forecastTSF

the predicted values, the out of sample forecasts and the values 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
N0<-9
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
forecastTSF (N0,Xt,bandwidth)

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