Using an estimate of the spectral density function for an input time series, Whittle's method fits the parameters of a specified SDF model to the data by optimizing an appropriate functional. In this case, the SDF for a fractionally differenced (FD) process model is used and an estimate of (delta), the FD parameter, is returned.
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x |
a vector containing a uniformly-sampled real-valued time series. |
... |
optional SDF estimation arguments passed directly to the |
dc |
a logical value. If |
delta.max |
the maximum value for the FD parameter to use in the
constrained optimization problem. Default: |
delta.min |
the minimum value for the FD parameter to use in the
constrained optimization problem. Default: |
freq.max |
the largerst normalized frequency of the SDFs use in the analysis.
Default: |
method |
a character string indicating the method to be used in estimating the Hurst coefficient (H). Choices are:
Default: |
sdf.method |
a character string denoting the method to use in estimating the SDF.
Choices are |
estimate of the FD parameter of the time series.
M. S. Taqqu and V. Teverovsky, On Estimating the Intensity of Long- Range Dependence in Finite and Infinite Variance Time Series (1998), in A practical Guide to Heavy Tails: Statistical Techniques and Applications, pp. 177–217, Birkhauser, Boston.
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