Description Usage Arguments Value Author(s) References Examples

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

1 | ```
forecastTSF(N0,Xt,bandwidth)
``` |

`N0` |
lead period of forecast |

`Xt` |
univariate time series |

`bandwidth` |
the bandwidth used in the regression equation |

`forecastTSF` |
the predicted values, the out of sample forecasts and the values of long memory parameter |

Sandipan Samanta, Ranjit Kumar Paul and Dipankar Mitra

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## 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)
``` |

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