Testing and Estimating Structural Breaks in Time Series and Panel Data in Stata
Yiannis Karavias () and
Joakim Westerlund ()
Discussion Papers from Department of Economics, University of Birmingham
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 2007â€“2008 financial crisis and the 2020 COVIDâ€“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 COVIDâ€“19 cases and deaths in the US using both country-level time series data and state-level panel data.
Keywords: Structural breaks; Time series data; Panel data; Cross-section dependence; COVIDâ€“19; xtbreak (search for similar items in EconPapers)
Pages: 27 pages
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Working Paper: Testing and Estimating Structural Breaks in Time Series and Panel Data in Stata (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:bir:birmec:21-14
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