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Identifying structural change is a crucial step in analysis of time seriesand panel data. The longer the time span, the higher the likelihood that themodel parameters have changed as a result of major disruptive events, such asthe 2007-2008 financial crisis and the 2020 COVID-19 outbreak. Detecting theexistence of breaks, and dating them is therefore necessary not only forestimation purposes but also for understanding drivers of change and theireffect on relationships. This article introduces a new community contributedcommand called xtbreak, which provides researchers with a complete toolbox foranalysing multiple structural breaks in time series and panel data. xtbreak candetect the existence of breaks, determine their number and location, andprovide break date confidence intervals. The new command is used to explorechanges in the relationship between COVID-19 cases and deaths in the US usingboth country-level time series data and state-level panel data.

Testing and Estimating Structural Breaks in Time Series and Panel Data in Stata