Dealing with structural breaks is an essential step in most empirical economic research. This is particularly true in panel data comprised of many cross-sectional units, which are all affected by major events. The COVID-19 pandemic has affected most sectors of the global economy; however, its impact on stock markets is still unclear. Most markets seem to have recovered while the pandemic is ongoing, suggesting that the relationship between stock returns and COVID-19 has been subject to structural break. It is therefore important to know if a structural break has occurred and, if it has, to infer the date of the break. Motivated by this last observation, the present article develops a new break detection toolbox that is applicable to different sized panels, easy to implement and robust to general forms of unobserved heterogeneity. The toolbox, which is the first of its kind, includes a structural change test, a break date estimator, and a break date confidence interval. Application to a panel covering 61 countries from January 3 to September 25, 2020, leads to the detection of a structural break that is dated to the first week of April. The effect of COVID-19 is negative before the break and zero thereafter, implying that while markets did react, the reaction was short-lived. A possible explanation is the quantitative easing programs announced by central banks all over the world in the second half of March. Supplementary materials for this article are available online.
Bibliographical noteFunding Information:
Westerlund would also like to thank the Knut and Alice Wallenberg Foundation for financial support through a Wallenberg Academy Fellowship. Acknowledgments
© 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.
- Structural Change
- Panel data
- Cross-section dependence
- Common correlated effects
- Structural change
ASJC Scopus subject areas
- Economics and Econometrics
- Statistics and Probability
- Social Sciences (miscellaneous)
- Statistics, Probability and Uncertainty