  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.
