IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v257y2025ics0165176525005385.html

Truncated History Framework for Synthetic Control Approaches

Author

Listed:
  • Spoelstra, Peter
  • Stolp, Tom
  • Golsteyn, Bart H.H.
  • Cornelisz, Ilja
  • van Klaveren, Chris

Abstract

We introduce the Truncated History (TH) framework for synthetic control approaches, which excludes pretreatment periods. The in-time placebo test fits within TH as right-truncation, removing final pretreatment periods. While useful, it may over-rely on early pretreatment data, potentially undermining robustness. To address this, we propose a left-truncation strategy, the left-TH robustness check, which iteratively excludes the earliest pretreatment periods and re-estimates effects. Stable results support credibility, while instability suggests that interval estimates may be more appropriate. TH provides robustness for pretreatment horizons and helps identify stable models, which we demonstrate on the California tobacco program of Abadie et al. (2010).

Suggested Citation

  • Spoelstra, Peter & Stolp, Tom & Golsteyn, Bart H.H. & Cornelisz, Ilja & van Klaveren, Chris, 2025. "Truncated History Framework for Synthetic Control Approaches," Economics Letters, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:ecolet:v:257:y:2025:i:c:s0165176525005385
    DOI: 10.1016/j.econlet.2025.112701
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176525005385
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2025.112701?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
    2. Alberto Abadie & Alexis Diamond & Jens Hainmueller, 2015. "Comparative Politics and the Synthetic Control Method," American Journal of Political Science, John Wiley & Sons, vol. 59(2), pages 495-510, February.
    3. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    4. Bruno Ferman & Cristine Pinto, 2021. "Synthetic controls with imperfect pretreatment fit," Quantitative Economics, Econometric Society, vol. 12(4), pages 1197-1221, November.
    5. Chen, Qiang & Yan, Guanpeng, 2023. "A mixed placebo test for synthetic control method," Economics Letters, Elsevier, vol. 224(C).
    6. Nikolay Doudchenko & Guido W. Imbens, 2016. "Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis," NBER Working Papers 22791, National Bureau of Economic Research, Inc.
    7. Dmitry Arkhangelsky & Guido Imbens, 2024. "Causal models for longitudinal and panel data: a survey," The Econometrics Journal, Royal Economic Society, vol. 27(3), pages 1-61.
    8. Alberto Abadie, 2021. "Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects," Journal of Economic Literature, American Economic Association, vol. 59(2), pages 391-425, June.
    9. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    10. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    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. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    2. Justin C. Wiltshire, 2023. "Walmart Supercenters and Monopsony Power: How A Large, Low-Wage Employer Impacts Local Labor Markets," Department Discussion Papers 2304, Department of Economics, University of Victoria.
    3. repec:ags:aaea22:335971 is not listed on IDEAS
    4. Kraynak,Daniel Christopher & Timilsina,Govinda R. & Alberini,Anna, 2024. "The Effect of Pricing Instruments on CO2 Emissions: Empirical Evidence from Australia," Policy Research Working Paper Series 10812, The World Bank.
    5. Kim, Youngho, 2023. "Payments for Ecosystem Services Programs and Climate Change Adaptation in Agriculture," 2023 Annual Meeting, July 23-25, Washington D.C. 335971, Agricultural and Applied Economics Association.
    6. Youngho Kim, 2024. "Payments for Ecosystem Services Programs and Climate Change Adaptation in Agriculture," Economics Series Working Papers 1054, University of Oxford, Department of Economics.
    7. Garoupa, Nuno & Spruk, Rok, 2025. "Populist constitutional backsliding and judicial independence: Evidence from Türkiye," International Review of Law and Economics, Elsevier, vol. 84(C).
    8. Lee, Yong-Jin Alex & Nilsson, Isabelle, 2025. "Estimating the effect of a state-level charging infrastructure funding program on plug-in electric vehicle adoption," Journal of Transport Geography, Elsevier, vol. 129(C).
    9. Cho, Sang-Wook (Stanley) & Wong, Sally, 2025. "Monetary regimes and regional economies: A counterfactual perspective from two euro opt-outs," Journal of Macroeconomics, Elsevier, vol. 85(C).
    10. Esaka, Taro & Fujii, Takao, 2026. "The lasting effect of yen-buying interventions: Two cases of Japanese FX interventions in 1997–98 and 2022," Journal of International Money and Finance, Elsevier, vol. 160(C).
    11. David Gilchrist & Thomas Emery & Nuno Garoupa & Rok Spruk, 2023. "Synthetic Control Method: A tool for comparative case studies in economic history," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 409-445, April.
    12. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    13. Crudu, Federico & Di Stefano, Roberta & Mellace, Giovanni & Tiezzi, Silvia, 2024. "The gray zone: How not imposing a strict lockdown at the beginning of a pandemic can cost many lives," Labour Economics, Elsevier, vol. 89(C).
    14. Fry, Joseph, 2024. "A method of moments approach to asymptotically unbiased Synthetic Controls," Journal of Econometrics, Elsevier, vol. 244(1).
    15. Guillaume Allaire Pouliot & Zhen Xie & Ziyi Liu, 2022. "Degrees of Freedom and Information Criteria for the Synthetic Control Method," Papers 2207.02943, arXiv.org, revised Mar 2026.
    16. Masahiro Kato & Akari Ohda, 2023. "Asymptotically Unbiased Synthetic Control Methods by Moment Matching," Papers 2307.11127, arXiv.org, revised Nov 2025.
    17. S. V. Doroshenko & R. I. Vasileva & V. P. Litvinets, 2024. "The Far Eastern Development Program and Effectiveness for Regions: Case-Study Assessment," Studies on Russian Economic Development, Springer, vol. 35(2), pages 237-245, April.
    18. Nuno Garoupa & Rok Spruk, 2025. "Revolutions as structural breaks: the long-term economic and institutional consequences of the 1979 Iranian Revolution," Constitutional Political Economy, Springer, vol. 36(3), pages 273-301, September.
    19. Chen, Qian & Koch, Christoffer & Richardson, Gary & Sharma, Padma, 2025. "Suspensions of payments and their consequences," Journal of Financial Stability, Elsevier, vol. 78(C).
    20. Taehyeon Koo & Zijian Guo, 2025. "Distributionally Robust Synthetic Control: Ensuring Robustness Against Highly Correlated Controls and Weight Shifts," Papers 2511.02632, arXiv.org, revised Jan 2026.
    21. Guanhao Zhou & Yuefeng Han & Xiufan Yu, 2025. "Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models," Papers 2505.20536, arXiv.org.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    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:eee:ecolet:v:257:y:2025:i:c:s0165176525005385. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.