Simulate Long-memory Time-series Data

DESCRIPTION:

Generates simulated long-memory time series data from a fractionally-differenced ARIMA model.

USAGE:

arima.fracdiff.sim(model, n = 10000) 

REQUIRED ARGUMENTS:

model
a list specifying a fractionally-differenced ARIMA model. The model should be a list with component names from: d, ar, ma, mu, where

d is the fractional differencing parameter (0 < d < .5). Default is 0.25

ar is the vector of autoregressive parameters. Default is NULL

ma is the vector of moving average parameters. Default is NULL

mu is the time series mean. Default is 0

OPTIONAL ARGUMENTS:

n
length of the time series to be simulated. Default is 10000

VALUE:

a vector representing the simulated univariate time series, with the model and random number seed as attributes.

REFERENCES:

Haslett, J. and Raftery, A. E.(1989). Space-time modeling with long-memory dependence: assessing Ireland's wind power resource (with discussion). Applied Statistics 38, 1-50.

The chapter "Analyzing Time Series" of the S-PLUS Guide to Statistical and Mathematical Analysis.

SEE ALSO:

, , .

EXAMPLES:

# generate a fractionally-differenced ARIMA(1,d,1) model  
# given initial values 
arima.fracdiff.sim(model = list(d = .3, ar = .2, ma = .4),  
                   n = 1000)