Object Reference : Object View and Procedure Reference : Series
  
 
waveanova
Perform wavelet variance decomposition of the series.
Syntax
Series View: series_name.waveanova(options)
Options
Basic Options
 
variance=arg (default = “nobias”)
Wavelet variance type: “nobias” (unbiased variance), “bias” (biased variance).
ci=arg (default = “none”)
Confidence interval type: “none” (no CIs computed), “gauss” (asymptotic normal), “chisq” (asymptotic chi-square), “blimit” (band-limited).
cilevel=arg (default = 0.95)
Confidence interval coverage as a number between 0 and 1.
prompt
Force the dialog to appear from within a program.
p
Print results.
Wavelet Transform Options
 
transform=arg (default=“dwt”)
Wavelet transform type: “dwt” (discrete wavelet transform – DWT), “modwt” (maximum overlap DWT – MODWT). Note that when performing DWT, if the series length is not dyadic, a dyadic fix may be set with the “fixlen=” option
fixlen=arg (default=“mean”)
Fix dyadic lengths in DWT: “zeros” (pad remainder with zeros), “mean” (pad remainder with mean of series), “median” (pad remainder with median of series), “shorten” (cut series length to dyadic length preceding series length).
maxscale=integer (default = max possible)
Maximum scale for wavelet transform.
The max possible is obtained as follows. Let denote the series length and decompose into its dyadic component and a remainder: , . The default maxscale is then set with the following rules:
DWT: (1) if then , otherwise (2) if expanding the series, and (3) if contracting the series .
MODWT: .
filter=arg (default=“h”)
Wavelet filter class: “h” (Haar), “d” (Daubechies), “la” (least asymmetric).
If “filter=h” or “filter=la”, the filter length may be specified using “flen=”.
Wavelet filter boundary conditions are specified using the “bound=” option
flen=integer
Wavelet filter excess length as an even number between 2 and 20.
For use when “filter=d” (default= 4) or “filter=la” (default=8).
bound=arg (default = “p”)
Filter boundary handling: “p” (periodic), “r” (reflective).
Examples
dgp.waveanova(maxscale=2)
The line above will perform wavelet decomposition of variance of the series DGP using a Haar wavelet filter and the unbiased variance form, up to the second wavelet scale.
dgp.waveanova(maxscale=5, ci=gauss)
The line above will perform wavelet decomposition of variance using an unbiased variance form. It will also produce a 95% confidence interval using asymptotic Gaussian critical values.
Cross-references
See “Wavelet Analysis” and “Wavelet Variance Decomposition” for discussion. See also “Wavelet Objects”.
See also Series::wavedecomp, Series::waveoutlier, Series::wavethresh, and Series::makewavelets.