Most Recent Changepoint Detection in Panel Data

Abstract

Detecting recent changepoints in time-series can be important for short-term prediction, as we can then base predictions just on the data since the changepoint. In many applications we have panel data, consisting of many related univariate time-series. We present a novel approach to detect sets of most recent changepoints in such panel data which aims to pool information across time-series, so that we preferentially infer a most recent change at the same time-point in multiple series. Our approach is computationally efficient as it involves analysing each time-series independently to obtain a profile-likelihood like quantity that summarises the evidence for the series having either no change or a specific value for its most recent changepoint. We then post-process this output from each time-series to obtain a potentially small set of times for the most recent changepoints, and, for each time, the set of series which has their most recent changepoint at that time. We demonstrate the usefulness of this method on two data sets: forecasting events in a telecommunications network and inference about changes in the net asset ratio for a panel of US firms.

State Tagging for Improved Earth and Environmental Data Quality Assurance

“…For instance, changepoints detection methods (Killick et al, 2012) can be used to identify jumps in the time series. State tagging efforts will benefit from recent advances in multivariate changepoints detection (Bardwell et al, 2019).…”

Early classification of spatio-temporal events using partial information

“…Multivariate change-point detection extends this framework to multiple time series measuring different quantities. Bardwell et al [ 22 ] consider multivariate change-point detection in a panel data setting. They define as the cost of segmenting time series i with the most recent change point r and minimize a penalized version of where K denotes the number of change-points, I k ⊂ {1, 2, …, N } and N the number of time series, such that for all time series i ∈ I k the most recent change-point is located at r k .…”

Change point estimation in panel data with time‐varying individual effects

“…The other part, including this paper, focuses on homogeneous panels, where the slope parameters are constant across individuals. In this setting, Vert and Bleakley (2010) propose estimating the change points via a group least‐angle approach; Qian and Su (2016) use an adaptive fused group Lasso (AFGL) method on the first‐differenced data; Li, Qian, and Su (2016) propose a principal component modified version of the AFGL method for dealing with a particular form of unobserved effects called interactive fixed effects; Baltagi, Kao, and Liu (2017) employ an ordinary least squares (OLS) method on the initial as well as the first‐differenced data for stationary and nonstationary regressors; Bardwell, Fearnhead, Eckley, Smith, and Spott (2018) use a minimum description length criterion.…”