Creates a realization of a timevarying fractionally differenced (FD) process with a given vector of FD parameters and corresponding vector of innovations variances.
1  FDSimulate(delta, innovations.var=1, method="ce", seed=0)

delta 
a vector containing timevarying FD parameters. 
innovations.var 
a numeric vector or scalar containing (timevarying) FD innovations variances.
If a scalar, the value is replicated appropriately. Otherwise, the length of this input should match the
length of the 
method 
a character string defining the method to use in forming the FD realization.
Choices are 
seed 
a positive integer representing the initial seed value to use
for the random number generator. If 
a vector containing a (timevarying) FD process realization corresponding to the input FD model parameters.
plot the output object. Optional arguments include:
Plot the simulated series. Default: TRUE
.
Plot the FD parameter as a function of time. Default: TRUE
.
Plot the innovations variance as a function of time. Default: TRUE
.
print the output object.
D. B. Percival and A. T. Walden, Wavelet Methods for Time Series Analysis, Cambridge University Press, 2000.
D. B. Percival and W.L.B. Constantine, Exact Simulations of TimeVarying Fractionally Differenced Processes, submitted to Journal of Computational and Graphical Statistics, 2002.
FDWhittle
, wavFDPBlock
, wavFDPTime
.
1 2 3 4 5 6 7 8 9 10 11 12  ## create a timevarying FD parameter, linearly
## varying from white to pink noise, then jump
## to a red noise plateau
delta < c(seq(0, 0.5, by=0.01), rep(1,100))
## set the innovations variance to unity
innovation < rep(1, length(delta))
## simulate a timevarying FD process
z < FDSimulate(delta=delta, innovation=innovation)
print(z)
plot(z)

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