  Identifying structural change is a crucial step in analysis of time series
and panel data. The longer the time span, the higher the likelihood that the
model parameters have changed as a result of major disruptive events, such as
the 2007-2008 financial crisis and the 2020 COVID-19 outbreak. Detecting the
existence of breaks, and dating them is therefore necessary not only for
estimation purposes but also for understanding drivers of change and their
effect on relationships. This article introduces a new community contributed
command called xtbreak, which provides researchers with a complete toolbox for
analysing multiple structural breaks in time series and panel data. xtbreak can
detect the existence of breaks, determine their number and location, and
provide break date confidence intervals. The new command is used to explore
changes in the relationship between COVID-19 cases and deaths in the US using
both country-level time series data and state-level panel data.
