acfrob.trim: Robust Autocorrelation Estimation Based on Trimming

In robts: Robust Time Series Analysis

Description

Robustly estimates the autocorrelation function of a time series based on a trimmed time series. See Dürre et al. (2015) for details.

This function is intended for internal usage only. Users should rather use the wrapper function acfrob with argument approach = "acftrim".

Usage

acfrob.trim(x, lag.max, trim = 0.1, biascorr = TRUE)

Arguments

x	univariate numeric vector or time series object.
lag.max	integer value giving the maximum lag at which to calculate the acf.
trim	numeric indicating the amount of trimmed values, see Details.
biascorr	logical indicating whether a bias correction of the result should be done. Note that this bias correction is only valid for Gaussian processes.

Details

The function basically first trims the smallest and largest observations of the time series and then calculates the usual sample acf. This procedure was proposed by Chan and Wei (1992).

trim determines the quantiles (alpha and 1-alpha) at which the data is trimmed. Therefore 2alpha percent of the data is trimmed, see Dürre et al. (2015) for details.

Since the estimator does not measure the usual autocorrelation, a nonlinear transformation based on simulations is applied, which guarantees consistency under Gaussian processes.

Value

A named list of the following elements:

acfvalues	Numeric vector of estimated autocorrelations at the lags 1,...,lag.max.
are	numeric value giving the asymptotic relative efficiency (ARE) of the estimator as compared to the classical nonrobust estimator, under the assumption that the observations are uncorrelated and from a Gaussian distribution. The ARE is currently not available for this estimation approach and is therefore NA.

Author(s)

Alexander Dürre, Tobias Liboschik and Jonathan Rathjens

References

Chan, W., and Wei, W. (1992): A comparison of some estimators of time series autocorrelations, Computational Statistics & Data Analysis, vol 14, .149–163, doi: 10.1016/0167-9473(92)90170-K.

Dürre, A., Fried, R. and Liboschik, T. (2015): Robust estimation of (partial) autocorrelation, Wiley Interdisciplinary Reviews: Computational Statistics, vol. 7, 205–222, doi: 10.1002/wics.1351.

See Also

The wrapper function acfrob.

Alternative acf subroutines: acfrob.GK, acfrob.filter, acfrob.multi, acfrob.partrank, acfrob.RA, acfrob.rank, acfrob.bireg.

Examples

set.seed(1066)
tss <- arima.sim(model = list(ar = 0.3, ma = 0.5), n = 100)
acfrob(tss, approach = "trim")

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