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

Westerlund, Joakim LU ; Karavias, Ioannis and Ditzen, Jan (2025) In Stata Journal 25(3). p.526-560
Abstract
Identifying structural change is a crucial step when analyzing time series and panel data. The longer the time span, the higher the likelihood that the model parameters have changed because 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 for not only estimation but also understanding drivers of change and their effect on relationships. In this article, we introduce a new community-contributed command called xtbreak, which provides researchers with a complete toolbox for analyzing multiple structural breaks in time series and panel data. xtbreak can detect the existence of breaks, determine their number and location,... (More)
Identifying structural change is a crucial step when analyzing time series and panel data. The longer the time span, the higher the likelihood that the model parameters have changed because 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 for not only estimation but also understanding drivers of change and their effect on relationships. In this article, we introduce a new community-contributed command called xtbreak, which provides researchers with a complete toolbox for analyzing 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. We use xtbreak in examples to explore changes in the relationship between COVID-19 cases and deaths in the US using both aggregate and state-level data and in the relationship between approval ratings and consumer confidence using a panel of eight countries. (Less)
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Stata Journal
volume
25
issue
3
pages
526 - 560
publisher
SAGE Publications
external identifiers
  • scopus:105014870414
ISSN
1536-867X
DOI
10.1177/1536867X251365449
language
English
LU publication?
yes
id
7c8dac6d-0f92-4d71-ba4d-8228759b0844
date added to LUP
2025-03-03 09:45:14
date last changed
2025-10-16 11:39:11
@article{7c8dac6d-0f92-4d71-ba4d-8228759b0844,
  abstract     = {{Identifying structural change is a crucial step when analyzing time series and panel data. The longer the time span, the higher the likelihood that the model parameters have changed because 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 for not only estimation but also understanding drivers of change and their effect on relationships. In this article, we introduce a new community-contributed command called xtbreak, which provides researchers with a complete toolbox for analyzing 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. We use xtbreak in examples to explore changes in the relationship between COVID-19 cases and deaths in the US using both aggregate and state-level data and in the relationship between approval ratings and consumer confidence using a panel of eight countries.}},
  author       = {{Westerlund, Joakim and Karavias, Ioannis and Ditzen, Jan}},
  issn         = {{1536-867X}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{526--560}},
  publisher    = {{SAGE Publications}},
  series       = {{Stata Journal}},
  title        = {{Testing and Estimating Structural Breaks in Time Series and Panel Data in Stata}},
  url          = {{http://dx.doi.org/10.1177/1536867X251365449}},
  doi          = {{10.1177/1536867X251365449}},
  volume       = {{25}},
  year         = {{2025}},
}