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hwwntest (version 1.3.2)

Tests of White Noise using Wavelets

Description

Provides methods to test whether time series is consistent with white noise. Two new tests based on Haar wavelets and general wavelets described by Nason and Savchev (2014) are provided and, for comparison purposes this package also implements the B test of Bartlett (1967) . Functionality is provided to compute an approximation to the theoretical power of the general wavelet test in the case of general ARMA alternatives.

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Version

Install

install.packages('hwwntest')

Monthly Downloads

356

Version

1.3.2

License

GPL-2

Maintainer

Guy Nason

Last Published

September 13th, 2023

Functions in hwwntest (1.3.2)

White noise test using general wavelets.

compute.rejection

Function to compute empirical size or power for various tests of white noise.

hwwntest-package

tools:::Rd_package_title("hwwntest")

Hybrid of Box-Ljung test, Bartlett B test, Haar wavelet and General wavelet tests.

Hybrid wavelet test of white noise.

Compute cumulative normalized periodogram.

Compute coefficients required for approximaing the wavelet transform using the square of wavelets.

Perform a test for white noise on a time series.

genwwn.powerplot

Plot (approximation) to the theoretical power of the genwwn.test test for ARMA processes (including, of course, white noise itself) for a range of sample sizes.

Compute the non-decimated squared wavelet transform.

Compute expansion with respect to squared wavelets.

Brute-force calculation of the non-decimated squared wavelet transform.

Bartlett's B test for white noise

Compute (approximation) to the theoretical power of the genwwn.test test for ARMA processes (including, of course, white noise itself).

Compute discrete wavelets

Compute the Macdonald density function for a specified parameter value m at a vector of x values.

Test for white noise based on the coarsest scale Haar wavelet coefficient of the spectrum.