asymvar.acfextra: Calculation of the Long Run Variance Based on an AR Fit

In robts: Robust Time Series Analysis

Description

Computes the long run variance, which is required for change point testing, by first fitting an AR-model and then extrapolating its theoretic autocovariance function.

Usage

asymvar.acfextra(x, obs = c("untransformed", "ranks"), order.max = 2,
                 aic = FALSE)

Arguments

x	numeric vector or univariate time series object.
obs	character string indicating whether the long run variance of a cusum statistic ("untransformed") or of a Wilcoxon statistic ("ranks") is calculated.
aic	logical indicating whether the AR order should be estimated by robust AIC criteria considering orders up to order.max (if aic = TRUE) or should be equal to order.max (if aic = FALSE).
order.max	integer value determining the (maximal) order of the AR fit.

Details

Cusum-type change point tests require an estimation of the long run variance. One possibility is to fit an AR model and calculate its corresponding autocovariance function. The long run variance is then estimated by the Bartlett estimator with bandwidth n-1.

Value

List containing the following named elements:

lrv	estimated long run variance
order	estimated order of the AR model

See Also

The long run variance can be also estimated by asymvar.window and asymvar.acf.

Examples

set.seed(1066)
tss <- arima.sim(model = list(ar = 0.3, ma = 0.5), n = 100)
asymvar.acfextra(tss)

robts documentation built on May 2, 2019, 4:55 p.m.