test.linear: Test Linearity of a Time Series via Normalized Bispectrum

In astsa: Applied Statistical Time Series Analysis

Test Linearity of a Time Series via Normalized Bispectrum

Description

Produces a plot of the tail probabilities of a normalized bispectrum of a series under the assumption the model is a linear process with iid innovations.

Usage

test.linear(series, color = TRUE, detrend = FALSE, main = NULL)

Arguments

series	the time series (univariate only)
color	if FALSE, the graphic is produced in gray scale
detrend	if TRUE, the series is detrended first
main	if NULL (default), a very nice title is chosen for the plot

Value

prob	matrix of tail probabilities - returned invisibly

Note

The null hypothesis is that the data are from a linear process with i.i.d. innovations. Under the null hypothesis, the bispectrum is constant over all frequencies. Chi-squared test statistics are formed in blocks to measure departures from the null hypothesis and the corresponding p-values are displayed in a graphic and returned invisibly. Details are in Hinich, M. and Wolinsky, M. (2005). Normalizing bispectra. Journal of Statistical Planning and Inference, 130, 405–411.

Author(s)

D.S. Stoffer

References

You can find demonstrations of astsa capabilities at FUN WITH ASTSA.

The most recent version of the package can be found at https://github.com/nickpoison/astsa/.

In addition, the News and ChangeLog files are at https://github.com/nickpoison/astsa/blob/master/NEWS.md.

The webpages for the texts and some help on using R for time series analysis can be found at https://nickpoison.github.io/.

Examples

## Not run: 
test.linear(nyse)  # :(
test.linear(soi)   # :)

## End(Not run)

astsa documentation built on May 29, 2024, 10:29 a.m.