TY - JOUR

T1 - Detecting changes in mean in the presence of time-varying autocovariance

AU - McGonigle, Euan

AU - Killick, Rebecca

AU - Nunes, Matthew

PY - 2021/1/15

Y1 - 2021/1/15

N2 - There has been much attention in recent years to the problem of detecting mean changes in a piecewise constant time series. Often, methods assume that the noise can be taken to be independent, identically distributed (IID), which in practice may not be a reasonable assumption. There is comparatively little work studying the problem of mean changepoint detection in time series with non‐trivial autocovariance structure. In this article, we propose a likelihood‐based method using wavelets to detect changes in mean in time series that exhibit time‐varying autocovariance. Our proposed technique is shown to work well for time series with a variety of error structures via a simulation study, and we demonstrate its effectiveness on two data examples arising in economics.

AB - There has been much attention in recent years to the problem of detecting mean changes in a piecewise constant time series. Often, methods assume that the noise can be taken to be independent, identically distributed (IID), which in practice may not be a reasonable assumption. There is comparatively little work studying the problem of mean changepoint detection in time series with non‐trivial autocovariance structure. In this article, we propose a likelihood‐based method using wavelets to detect changes in mean in time series that exhibit time‐varying autocovariance. Our proposed technique is shown to work well for time series with a variety of error structures via a simulation study, and we demonstrate its effectiveness on two data examples arising in economics.

U2 - 10.1002/sta4.351

DO - 10.1002/sta4.351

M3 - Journal article

VL - 10

JO - Stat

JF - Stat

SN - 2049-1573

IS - 1

M1 - e351

ER -