| Type: | Package |
| Title: | Two Stage Forecasting (TSF) for Long Memory Time Series in Presence of Structural Break |
| Version: | 0.1.1 |
| Author: | Sandipan Samanta, Ranjit Kumar Paul and Dipankar Mitra |
| Maintainer: | Dr. Ranjit Kumar Paul <ranjitstat@gmail.com> |
| Description: | Forecasting of long memory time series in presence of structural break by using TSF algorithm by Papailias and Dias (2015) <doi:10.1016/j.ijforecast.2015.01.006>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| Imports: | stats, fracdiff, forecast |
| LazyData: | TRUE |
| NeedsCompilation: | no |
| Packaged: | 2017-07-15 06:50:30 UTC; ranjitstat |
| Repository: | CRAN |
| Date/Publication: | 2017-07-15 06:49:07 UTC |
Predicting fractionally differenced 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$series)
## Forecasting using TSF method
StructuralBrekwithLongmemory(Xt,bandwidth)
Fractionally differenced series for any value of d
Description
The function fdseries computes the fractional differenced series for any value of d i.e. positive or negetive.
Usage
fdseries(x, d)Arguments
x |
univariate time series |
d |
The orer of fractional differencing to be done |
Value
fdseries |
fractionally differenced series for both positive as well as negetive d |
Author(s)
Sandipan Samanta, Ranjit Kumar Paul and Dipankar Mitra
References
Papailias, F. and Dias, G. F. 2015. Forecasting long memory series subject to structural change: A two-stage approach. International Journal of Forecasting, 31, 1056 to 1066.
Wang, C. S. H., Bauwens, L. and Hsiao, C. 2013. Forecasting a long memory process subject to structural breaks. Journal of Econometrics, 177, 171-184.
Reisen, V. A. (1994) Estimation of the fractional difference parameter in the ARFIMA(p,d,q) model using the smoothed periodogram. Journal Time Series Analysis, 15(1), 335 to 350.
Examples
## Simulating Long Memory Series
N <- 1000
PHI <- 0.2
THETA <- 0.1
SD <- 1
M <- 0
D <- 0.2
Seed <- 123
set.seed(Seed)
Sim.Series <- fracdiff::fracdiff.sim(n = N, ar = c(PHI), ma = c(THETA),
d = D, rand.gen = rnorm, sd = SD, mu = M)
Xt <- as.ts(Sim.Series$series)
## fractional differencing
fdseries(Xt,d=D)
Forecasting fractionally differenced series using TSF approach
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)