IDEAS home Printed from https://ideas.repec.org/a/taf/jnlbes/v41y2023i3p653-666.html
   My bibliography  Save this article

Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19

Author

Listed:
  • Yiannis Karavias
  • Paresh Kumar Narayan
  • Joakim Westerlund

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 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.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:jnlbes:v:41:y:2023:i:3:p:653-666
    DOI: 10.1080/07350015.2022.2053690
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07350015.2022.2053690
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/07350015.2022.2053690?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Lu, Xun & Su, Liangjun, 2016. "Shrinkage estimation of dynamic panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 190(1), pages 148-175.
    2. Tim Bollerslev & George Tauchen & Hao Zhou, 2009. "Expected Stock Returns and Variance Risk Premia," Review of Financial Studies, Society for Financial Studies, vol. 22(11), pages 4463-4492, November.
    3. Zhang, Dayong & Hu, Min & Ji, Qiang, 2020. "Financial markets under the global pandemic of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    4. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    5. Otilia Boldea & Bettina Drepper & Zhuojiong Gan, 2020. "Change point estimation in panel data with time‐varying individual effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 712-727, September.
    6. Bai, Jushan, 1997. "Estimating Multiple Breaks One at a Time," Econometric Theory, Cambridge University Press, vol. 13(3), pages 315-352, June.
    7. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    8. Kent Daniel & David Hirshleifer & Avanidhar Subrahmanyam, 1998. "Investor Psychology and Security Market Under- and Overreactions," Journal of Finance, American Finance Association, vol. 53(6), pages 1839-1885, December.
    9. Hidalgo, Javier & Schafgans, Marcia, 2017. "Inference and testing breaks in large dynamic panels with strong cross sectional dependence," Journal of Econometrics, Elsevier, vol. 196(2), pages 259-274.
    10. Bai, Jushan, 2010. "Common breaks in means and variances for panel data," Journal of Econometrics, Elsevier, vol. 157(1), pages 78-92, July.
    11. Pesaran, M. Hashem & Tosetti, Elisa, 2011. "Large panels with common factors and spatial correlation," Journal of Econometrics, Elsevier, vol. 161(2), pages 182-202, April.
    12. Joakim Westerlund & Hande Karabiyik & Paresh Narayan, 2017. "Testing for Predictability in panels with General Predictors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 554-574, April.
    13. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Kyle J. Kost & Marco C. Sammon & Tasaneeya Viratyosin, 2020. "The Unprecedented Stock Market Impact of COVID-19," NBER Working Papers 26945, National Bureau of Economic Research, Inc.
    14. Pesaran, M. Hashem & Vanessa Smith, L. & Yamagata, Takashi, 2013. "Panel unit root tests in the presence of a multifactor error structure," Journal of Econometrics, Elsevier, vol. 175(2), pages 94-115.
    15. Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November.
    16. Karabiyik, Hande & Reese, Simon & Westerlund, Joakim, 2017. "On the role of the rank condition in CCE estimation of factor-augmented panel regressions," Journal of Econometrics, Elsevier, vol. 197(1), pages 60-64.
    17. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    18. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2016. "Estimation of heterogeneous panels with structural breaks," Journal of Econometrics, Elsevier, vol. 191(1), pages 176-195.
    19. Degui Li & Junhui Qian & Liangjun Su, 2016. "Panel Data Models With Interactive Fixed Effects and Multiple Structural Breaks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1804-1819, October.
    20. Ashraf, Badar Nadeem, 2020. "Economic impact of government interventions during the COVID-19 pandemic: International evidence from financial markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    21. Badi H. Baltagi & Chihwa Kao & Long Liu, 2017. "Estimation and identification of change points in panel models with nonstationary or stationary regressors and error term," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 85-102, March.
    22. M. Hashem Pesaran, 2007. "A simple panel unit root test in the presence of cross-section dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
    23. Alessandro Rebucci & Jonathan S. Hartley & Daniel Jiménez, 2022. "An Event Study of COVID-19 Central Bank Quantitative Easing in Advanced and Emerging Economies," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, volume 43, pages 291-322, Emerald Group Publishing Limited.
    24. Jaromír Antoch & Jan Hanousek & Lajos Horváth & Marie Hušková & Shixuan Wang, 2019. "Structural breaks in panel data: Large number of panels and short length time series," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 828-855, August.
    25. Gunther Capelle-Blancard & Adrien Desroziers, 2020. "The stock market is not the economy? Insights from the Covid-19 crisis," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03252106, HAL.
    26. Bollerslev, Tim & Xu, Lai & Zhou, Hao, 2015. "Stock return and cash flow predictability: The role of volatility risk," Journal of Econometrics, Elsevier, vol. 187(2), pages 458-471.
    27. Alexander F. Wagner, 2020. "What the stock market tells us about the post-COVID-19 world," Nature Human Behaviour, Nature, vol. 4(5), pages 440-440, May.
    28. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    29. Dornbusch, Rudiger & Fischer, Stanley, 1980. "Exchange Rates and the Current Account," American Economic Review, American Economic Association, vol. 70(5), pages 960-971, December.
    30. Salisu, Afees A. & Vo, Xuan Vinh, 2020. "Predicting stock returns in the presence of COVID-19 pandemic: The role of health news," International Review of Financial Analysis, Elsevier, vol. 71(C).
    31. Harrison Hong & Jeremy C. Stein, 1999. "A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets," Journal of Finance, American Finance Association, vol. 54(6), pages 2143-2184, December.
    32. Hidalgo, Javier & Schafgans, Marcia, 2017. "Inference and testing breaks in large dynamic panels with strong cross sectional dependence," LSE Research Online Documents on Economics 68839, London School of Economics and Political Science, LSE Library.
    33. Erdem, Orhan, 2020. "Freedom and stock market performance during Covid-19 outbreak," Finance Research Letters, Elsevier, vol. 36(C).
    34. repec:hal:journl:peer-00796743 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bianca Raluca Baditoiu & Roxana Ioan & Valentin Partenie Munteanu & Alexandru Buglea, 2023. "Investors’ reactions on the publication of integrated reports. Evidence from European stock markets," E&M Economics and Management, Technical University of Liberec, Faculty of Economics, vol. 26(2), pages 158-171, June.
    2. Jan Ditzen & Yiannis Karavias & Joakim Westerlund, 2021. "Testing and Estimating Structural Breaks in Time Series and Panel Data in Stata," Papers 2110.14550, arXiv.org, revised Oct 2021.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jan Ditzen & Yiannis Karavias & Joakim Westerlund, 2022. "Multiple Structural Breaks in Interactive Effects Panel Data and the Impact of Quantitative Easing on Bank Lending," Papers 2211.06707, arXiv.org, revised Jan 2023.
    2. Jiang, Peiyun & Kurozumi, Eiji, 2021. "A new test for common breaks in heterogeneous panel data models," Discussion paper series HIAS-E-107, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    3. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    4. Bai, Jushan & Han, Xu & Shi, Yutang, 2020. "Estimation and inference of change points in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 219(1), pages 66-100.
    5. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2016. "Estimation of heterogeneous panels with structural breaks," Journal of Econometrics, Elsevier, vol. 191(1), pages 176-195.
    6. Badi H. Baltagi & Qu Feng & Chihwa Kao, 2019. "Structural changes in heterogeneous panels with endogenous regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 883-892, September.
    7. Qian, Junhui & Su, Liangjun, 2016. "Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso," Journal of Econometrics, Elsevier, vol. 191(1), pages 86-109.
    8. Otilia Boldea & Bettina Drepper & Zhuojiong Gan, 2020. "Change point estimation in panel data with time‐varying individual effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 712-727, September.
    9. Cem Ertur & Antonio Musolesi, 2017. "Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 477-503, April.
    10. Smith, Simon C. & Timmermann, Allan & Zhu, Yinchu, 2019. "Variable selection in panel models with breaks," Journal of Econometrics, Elsevier, vol. 212(1), pages 323-344.
    11. Ye, Xiaoqing & Xu, Juan & Wu, Xiangjun, 2018. "Estimation of an unbalanced panel data Tobit model with interactive effects," Journal of choice modelling, Elsevier, vol. 28(C), pages 108-123.
    12. Okui, Ryo & Wang, Wendun, 2021. "Heterogeneous structural breaks in panel data models," Journal of Econometrics, Elsevier, vol. 220(2), pages 447-473.
    13. Tim Buyse & Freddy Heylen & Ruben Schoonackers, 2015. "On The Role Of Public Policies And Wage Formation For Private Investment In R&D: A Long-Run Panel Analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 15/911, Ghent University, Faculty of Economics and Business Administration.
    14. Alharbi, Samar S. & Al Mamun, Md & Boubaker, Sabri & Rizvi, Syed Kumail Abbas, 2023. "Green finance and renewable energy: A worldwide evidence," Energy Economics, Elsevier, vol. 118(C).
    15. Lumsdaine, Robin L. & Okui, Ryo & Wang, Wendun, 2023. "Estimation of panel group structure models with structural breaks in group memberships and coefficients," Journal of Econometrics, Elsevier, vol. 233(1), pages 45-65.
    16. Perry Singleton, 2015. "Health, Medical Innovation, and Disability Insurance: A Care Study of HIV Antiretroviral Therapy," Center for Policy Research Working Papers 182, Center for Policy Research, Maxwell School, Syracuse University.
    17. Sean Holly & Ivan Petrella, 2012. "Factor Demand Linkages, Technology Shocks, and the Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 948-963, November.
    18. Baltagi, Badi H. & Li, Jing, 2015. "Cointegration of matched home purchases and rental price indexes — Evidence from Singapore," Regional Science and Urban Economics, Elsevier, vol. 55(C), pages 80-88.
    19. Tiago Neves Sequeira & Marcelo Santos & Alexandra Ferreira-Lopes, 2017. "Income Inequality, TFP, and Human Capital," The Economic Record, The Economic Society of Australia, vol. 93(300), pages 89-111, March.
    20. Markus Eberhardt & Andrea Filippo Presbitero, 2013. "This Time They're Different: Heterogeneity;and Nonlinearity in the Relationship;between Debt and Growth," Mo.Fi.R. Working Papers 92, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:jnlbes:v:41:y:2023:i:3:p:653-666. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UBES20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.