weakly.stationary: Testing for Weak Stationarity in a Time Series

In analytics: Regression Outlier Detection, Stationary Bootstrap, Testing Weak Stationarity, NA Imputation, and Other Tools for Data Analysis

Description

Performs a series of statistical tests aimed at detecting non-stationarity.

Usage

weakly.stationary(tseries, signific_gen = 0.05, signific_pp.df = 0.05,
  MK = FALSE, BP = TRUE, PSR = TRUE, weak.dep = FALSE,
  mode = "neutral")

Arguments

tseries	a 1-D or 2-D array. In the latter case, the time series to be evaluated must be placed in the 2nd dimension (columns). If that's not your case, transpose it.
signific_gen	significance level for all tests except Phillips-Perron and Augmented Dickey-Fuller.
signific_pp.df	significance level for the Phillips-Perron and Augmented Dickey-Fuller tests.
MK	if TRUE, the Mann-Kendall test for constant mean is executed, instead of a faster basic test. Default is FALSE.
BP	if TRUE, the Breusch-Pagan test for constant (residual) variance is executed on the residuals of an auxiliary linear model that includes a time variable, instead of the McLeod-Li test. The default is TRUE.
PSR	if TRUE, the Priestley-Subba Rao test for nonstationarity across time is executed. The default is TRUE.
weak.dep	if TRUE, then the Phillips-Perron, Augmented Dickey-Fuller, and KPSS tests for weak stationarity (assuming an AR(p)) are performed.
mode	one of "neutral", "strict", "loose". Case insenstive. The default is "neutral".

Details

This function offers a great deal of customization: diverse significance levels, multiple tests specialized in certain aspects of (weak) stationarity, as well as handy predefined sets of parameters providing a more or less strict diagnostic: "neutral", "strict" and "loose" modes. By including this possibility, the technical burden on the user is made lighter. Mode "strict" includes two tests for constant mean (basic & Mann-Kendall), two tests for constant variance (McLeod-Li & Breusch-Pagan tests), the Priestley-Subba Rao (PSR) test for nonstationarity across time, and three tests for weak dependence (Phillips-Perron, Augmented Dickey-Fuller, and KPSS tests), which test weak stationarity if and only if the underlying data generating process is assumed to be an AR(p). Mode "loose" just performs the basic test for constant mean (a linear model that includes a trend whose statistical significance is determined using robust regression if the Durbin Watson test detects serial correlation in the residuals), and the Breusch-Pagan test (on the previous auxiliary linear model's residuals) for constant variance. Mode "neutral" (the default) provides all the default parameter options. Significance levels also differ across modes. This function differentiates two significance levels: general (signific_gen) and specific to the Phillips-Perron and Augmented Dickey-Fuller tests (signific_pp.df). In mode "strict", signific_gen is 0.1, and signific_pp.df is 0.01. In mode "loose", signific_gen is 0.01, and signific_pp.df is irrelevant. In mode "neutral", both significance levels are set to 0.05.

Value

if a 1-D array is supplied, then a Boolean is returned indicating whether the time series supplied is weakly stationary (TRUE) or not (FALSE). If a 2-D array is supplied, then a vector of Booleans is returned indicating whether each individual time series supplied is weakly stationary (TRUE) or not (FALSE).

Author(s)

Albert Dorador

Examples

x1 <- rnorm(1e3)
weakly.stationary(tseries = x1)
weakly.stationary(tseries = x1, signific_gen = 0.025)
weakly.stationary(tseries = x1, signific_pp.df = 0.1)
weakly.stationary(tseries = x1, MK = TRUE)
weakly.stationary(tseries = x1, PSR = FALSE)
weakly.stationary(tseries = x1, weak.dep = TRUE)
weakly.stationary(tseries = x1, MK = TRUE, PSR = FALSE)
weakly.stationary(tseries = x1, mode = "strict")
weakly.stationary(tseries = x1, mode = "loose")

require(stats)
set.seed(123)
x2 <- arima.sim(n = 1e3, list(ar = 0.4))
weakly.stationary(tseries = x2)
weakly.stationary(tseries = x2, signific_gen = 0.01)
weakly.stationary(tseries = x2, MK = TRUE)
weakly.stationary(tseries = x2, PSR = FALSE)
weakly.stationary(tseries = x2, weak.dep = TRUE)
weakly.stationary(tseries = x2, MK = TRUE, PSR = FALSE)
weakly.stationary(tseries = x2, mode = "strict")
weakly.stationary(tseries = x2, mode = "loose")

analytics documentation built on May 2, 2019, 3:37 p.m.