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

Session

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 talk will introduce an updated version of xtbreak and discuss use, options and capabilities of xtbreak. First, the relevant econometric theory will be revisited followed by empirical examples. Emphasis will be put on challenges using xtbreak in panel data, how to interpret results and speed improvements using Python.