Simulation of an FD process with time varying model parameters.

USAGE:

wavFDPSimulate( delta = c(0.2, 0.4), innovation = rep(1,2) )

DESCRIPTION:

Creates a realization of a time-varying fractionally differenced (FD) process with a given vector of FD parameters and corresponding vector of innovations variances.

REQUIRED ARGUMENTS:

delta
A vector containing time-varying FD parameters.

OPTIONAL ARGUMENTS:

innovations.variance
A vector containing (time-varying) FD innovations variances. Default: a vector the same length as delta and filled with ones.

VALUE:

result
A vector containing a (time-varying) FD process realization corresponding to the input FD model parameters.

REFERENCES:

(1) D. B. Percival and A. T. Walden, ``Wavelet Methods for Time Series Analysis'', Cambridge University Press, 2000.

(2) D. B. Percival and W.L.B. Constantine, ``Exact Simulations of Time-Varying Fractionally Differenced Processes'', submitted to Journal of Computational and Graphical Statistics, 2002.

SEE ALSO:

, , .

EXAMPLES:

    ## create a time-varying FD parameter,
    ## linearly varying from white to pink noise
    delta <- seq( 0, 0.5, by = 0.02 )

    ## set the innovations variance to unity
    innovation <- rep(1, length( delta ) )

    ## simulate a time-varying FD process
    result <- wavFDPSimulate( delta = delta,
    + innovation = innovation )