dbacf_AR1: Robust dbacf in change point regression with AR(1) errors

In dbacf: Autocovariance Estimation via Difference-Based Methods

Robust dbacf in change point regression with AR(1) errors

Description

In the context of change point regression with a stationary AR(1) error process, this function estimates the autoregressive coefficient along with the autocovariance/correlation function as a function of given lags.

Usage

dbacf_AR1(data, type = c("covariance", "correlation"), lags)

Arguments

data	numeric vector or a univariate object of class ts.
type	character string specifying whether covariance (default) or correlation must be computed.
lags	numeric giving the number of lags to compute.

Value

An object of class "dbacf" containing:

• acf numeric vector of length lags + 1 giving estimated (auto)covariance/correlation function

• rho numeric, estimate of autoregressive coefficient

• acfType string indicating whether covariance or correlation has been computed

• n integer giving length(data)

References

Chakar, S. and Lebarbier, E. and Lévy-Leduc, C. and Robin, S. (2017). A robust approach for estimating change-points in the mean of an AR(1) process, Bernoulli, 23(2), 1408-1447

Examples

ar1 <- arima.sim(n = 50, model = list(ar = c(0.5), order = c(1, 0, 0)), 
                 sd = 0.25)
dbacf_AR1(ar1, type="correlation", lags=10)

dbacf documentation built on July 9, 2023, 6:26 p.m.