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

Author

Listed:
  • Jan Ditzen

    (Free University of Bozen-Bolzano)

  • Yiannis Karavias

    (University of Birmingham)

  • Joakim Westerlund

    (Lund University)

Abstract

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.

Suggested Citation

  • Jan Ditzen & Yiannis Karavias & Joakim Westerlund, 2021. "Testing and Estimating Structural Breaks in Time Series and Panel Data in Stata," Discussion Papers 21-14, Department of Economics, University of Birmingham.
    • Handle: RePEc:bir:birmec:21-14
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    References listed on IDEAS

    as
    1. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    2. Jan Ditzen, 2018. "Estimating dynamic common-correlated effects in Stata," Stata Journal, StataCorp LP, vol. 18(3), pages 585-617, September.
    3. Yiannis Karavias & Paresh Kumar Narayan & Joakim Westerlund, 2023. "Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(3), pages 653-666, July.
    4. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
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