IDEAS home Printed from https://ideas.repec.org/a/bla/jecsur/v37y2023i2p302-320.html

Measuring stability and structural breaks: Applications in social sciences

Author

Listed:
  • Daria Loginova
  • Stefan Mann

Abstract

Several theoretical and methodological works have helped to clarify a number of principles of data analysis. The application of this knowledge in social studies when investigating stability and structural breaks needs a clear roadmap. This article suggests methodological frameworks that use structured knowledge on volatility and stability. It explains principles of measure selection per se and regarding research interest and relates these principles to statistical methods. The paper makes recommendations for practitioners concerned with variation issues on model and method selection.

Suggested Citation

  • Daria Loginova & Stefan Mann, 2023. "Measuring stability and structural breaks: Applications in social sciences," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 302-320, April.
    • Handle: RePEc:bla:jecsur:v:37:y:2023:i:2:p:302-320
    DOI: 10.1111/joes.12505
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/joes.12505
    Download Restriction: no
    File URL: https://libkey.io/10.1111/joes.12505?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
    ---><---

    References listed on IDEAS

    as
    1. Filomena Maggino & Carolina Facioni, 2017. "Measuring Stability and Change: Methodological Issues in Quality of Life studies," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(1), pages 161-187, January.
    2. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, IZA Network @ LISER.
    3. David A. Jaeger & Theodore J. Joyce & Robert Kaestner, 2020. "A Cautionary Tale of Evaluating Identifying Assumptions: Did Reality TV Really Cause a Decline in Teenage Childbearing?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 317-326, April.
    4. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
    7. Daria Loginova & Marco Portmann & Martin Huber, 2021. "Assessing the Effects of Seasonal Tariff‐rate Quotas on Vegetable Prices in Switzerland," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(2), pages 607-627, June.
    8. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    9. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
    10. Filomena Maggino & Carolina Facioni, 2017. "Erratum to: Measuring Stability and Change: Methodological Issues in Quality of Life Studies," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(3), pages 1297-1297, February.
    11. Dabin Wang & William G. Tomek, 2007. "Commodity Prices and Unit Root Tests," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(4), pages 873-889.
    12. Catherine Hausman & David S. Rapson, 2018. "Regression Discontinuity in Time: Considerations for Empirical Applications," Annual Review of Resource Economics, Annual Reviews, vol. 10(1), pages 533-552, October.
    13. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    14. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    15. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    16. Gunnar Myrdal, 1974. "What Is Development?," Journal of Economic Issues, Taylor & Francis Journals, vol. 8(4), pages 729-736, December.
    17. David Dollar & Craig Burnside, 2000. "Aid, Policies, and Growth," American Economic Review, American Economic Association, vol. 90(4), pages 847-868, September.
    18. Pinches, George E & Kinney, William R, Jr, 1971. "The Measurement of the Volatility of Common Stock Prices," Journal of Finance, American Finance Association, vol. 26(1), pages 119-125, March.
    19. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, vol. 142(2), pages 615-635, February.
    20. David S. Lee & Thomas Lemieux, 2010. "Regression Discontinuity Designs in Economics," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 281-355, June.
    21. Sam Asher & Denis Nekipelov & Paul Novosad & Stephen P. Ryan, 2016. "Classification Trees for Heterogeneous Moment-Based Models," NBER Working Papers 22976, National Bureau of Economic Research, Inc.
    22. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    23. Pierre Perron, 2005. "Dealing with Structural Breaks," Boston University - Department of Economics - Working Papers Series WP2005-017, Boston University - Department of Economics.
    Full references (including those not matched with items on IDEAS)

    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. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
    2. Andrew J. Patton & Yasin Simsek, 2023. "Generalized Autoregressive Score Trees and Forests," Papers 2305.18991, arXiv.org.
    3. Liu, Zhaoda & Hou, Wanyue & Li, Zixian & Shi, Peiyao, 2025. "Price limits, investor asset allocation, and price volatility: Evidence from China’s registration-based IPO reform," Research in International Business and Finance, Elsevier, vol. 75(C).
    4. KAMKOUM, Arnaud Cedric, 2023. "The Federal Reserve’s Response to the Global Financial Crisis and its Effects: An Interrupted Time-Series Analysis of the Impact of its Quantitative Easing Programs," Thesis Commons d7pvg, Center for Open Science.
    5. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    6. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    7. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    8. Florian Neumeier, 2018. "Do Businessmen Make Good Governors?," Economic Inquiry, Western Economic Association International, vol. 56(4), pages 2116-2136, October.
    9. Sascha O. Becker & Peter H. Egger & Maximilian von Ehrlich, 2013. "Absorptive Capacity and the Growth and Investment Effects of Regional Transfers: A Regression Discontinuity Design with Heterogeneous Treatment Effects," American Economic Journal: Economic Policy, American Economic Association, vol. 5(4), pages 29-77, November.
    10. Daniel Boller & Michael Lechner & Gabriel Okasa, 2025. "The effect of sport in online dating: evidence from causal machine learning," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-13, December.
    11. Pfeifer, Gregor & Stockburger, Mirjam, 2023. "The morning after: Prescription-free access to emergency contraceptive pills," Journal of Health Economics, Elsevier, vol. 91(C).
    12. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed," Labour Economics, Elsevier, vol. 65(C).
    13. Arne Henningsen & Guy Low & David Wuepper & Tobias Dalhaus & Hugo Storm & Dagim Belay & Stefan Hirsch, 2026. "Estimating Causal Effects With Observational Data: Guidelines for Agricultural and Applied Economists," Journal of Agricultural Economics, Wiley Blackwell, vol. 77(2), pages 356-382, June.
    14. Alessandra Amendola & Vincenzo Candila & Antonio Naimoli & Giuseppe Storti, 2024. "Combining Value-at-Risk and Expected Shortfall forecasts via the Model Confidence Set," Papers 2406.06235, arXiv.org, revised Feb 2026.
    15. Kheiravar, Khaled H, 2019. "Economic and Econometric Analyses of the World Petroleum Industry, Energy Subsidies, and Air Pollution," Institute of Transportation Studies, Working Paper Series qt3gj151w9, Institute of Transportation Studies, UC Davis.
    16. Lee, Wang-Sheng & Tran, Trang My & Yu, Lamont Bo, 2023. "Green infrastructure and air pollution: Evidence from highways connecting two megacities in China," Journal of Environmental Economics and Management, Elsevier, vol. 122(C).
    17. Gabriella Conti & Rita Ginja & Petra Persson & Barton Willage, 2024. "The menopause "penalty"," IFS Working Papers W24/05, Institute for Fiscal Studies.
    18. Goller, Daniel & Lechner, Michael & Pongratz, Tamara & Wolff, Joachim, 2025. "Active labor market policies for the long-term unemployed: New evidence from causal machine learning," Labour Economics, Elsevier, vol. 94(C).
    19. Arnaud Cedric Kamkoum, 2023. "The Federal Reserve's Response to the Global Financial Crisis and Its Long-Term Impact: An Interrupted Time-Series Natural Experimental Analysis," Papers 2305.12318, arXiv.org.
    20. Murathi Kiratu, Nixon, 2014. "An Assessment of the Impact of Kilimo Plus Subsidy Program on Smallholder Farmers' Food Security and Income in Nakuru North District, Kenya," Research Theses 243470, Collaborative Masters Program in Agricultural and Applied Economics.

    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:bla:jecsur:v:37:y:2023:i:2:p:302-320. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0950-0804 .

    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.