Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19
(2023) In Journal of Business and Economic Statistics 41(3). p.653-666- Abstract
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... (More)
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.
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- author
- Karavias, Yiannis ; Narayan, Paresh Kumar and Westerlund, Joakim LU
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Change-point, Common correlated effects, COVID-19, Cross-section dependence, Panel data, Structural change
- in
- Journal of Business and Economic Statistics
- volume
- 41
- issue
- 3
- pages
- 653 - 666
- publisher
- American Statistical Association
- external identifiers
-
- scopus:85129224641
- scopus:85129224641
- ISSN
- 0735-0015
- DOI
- 10.1080/07350015.2022.2053690
- language
- English
- LU publication?
- yes
- id
- 796d10e6-f04f-470c-91bf-40ae6be3f052
- date added to LUP
- 2022-03-17 08:52:08
- date last changed
- 2023-10-26 14:59:05
@article{796d10e6-f04f-470c-91bf-40ae6be3f052,
abstract = {{<p>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.</p>}},
author = {{Karavias, Yiannis and Narayan, Paresh Kumar and Westerlund, Joakim}},
issn = {{0735-0015}},
keywords = {{Change-point; Common correlated effects; COVID-19; Cross-section dependence; Panel data; Structural change}},
language = {{eng}},
number = {{3}},
pages = {{653--666}},
publisher = {{American Statistical Association}},
series = {{Journal of Business and Economic Statistics}},
title = {{Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19}},
url = {{http://dx.doi.org/10.1080/07350015.2022.2053690}},
doi = {{10.1080/07350015.2022.2053690}},
volume = {{41}},
year = {{2023}},
}