IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v65y2024i1d10.1007_s00362-022-01375-z.html
   My bibliography  Save this article

On the use of historical estimates

Author

Listed:
  • Ori Davidov

    (University of Haifa)

  • Tamás Rudas

    (Eötvös Loránd University)

Abstract

The use of historical, i.e., already existing, estimates in current studies is common in a wide variety of application areas. Nevertheless, despite their routine use, the uncertainty associated with historical estimates is rarely properly accounted for in the analysis. In this communication, we review common practices and then provide a mathematical formulation and a principled frequentist methodology for addressing the problem of drawing inferences in the presence of historical estimates. Three distinct variants are investigated in detail; the corresponding limiting distributions are found and compared. The design of future studies, given historical data, is also explored and relations with a variety of other well-studied statistical problems discussed.

Suggested Citation

  • Ori Davidov & Tamás Rudas, 2024. "On the use of historical estimates," Statistical Papers, Springer, vol. 65(1), pages 203-236, February.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:1:d:10.1007_s00362-022-01375-z
    DOI: 10.1007/s00362-022-01375-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00362-022-01375-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00362-022-01375-z?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Nele Goeyvaerts & Niel Hens & Benson Ogunjimi & Marc Aerts & Ziv Shkedy & Pierre Van Damme & Philippe Beutels, 2010. "Estimating infectious disease parameters from data on social contacts and serological status," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 255-277, March.
    3. Jae Kwang Kim & J. N. K. Rao, 2012. "Combining data from two independent surveys: a model-assisted approach," Biometrika, Biometrika Trust, vol. 99(1), pages 85-100.
    4. Yi‐Hau Chen & Hung Chen, 2000. "A unified approach to regression analysis under double‐sampling designs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(3), pages 449-460.
    5. Eugene Demidenko & Todd W Miller, 2019. "Statistical determination of synergy based on Bliss definition of drugs independence," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-22, November.
    6. Vaart,A. W. van der, 2000. "Asymptotic Statistics," Cambridge Books, Cambridge University Press, number 9780521784504, June.
    7. Peddada, Shyamal D. & Dinse, Gregg E. & Kissling, Grace E., 2007. "Incorporating Historical Control Data When Comparing Tumor Incidence Rates," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1212-1220, December.
    8. Ridder, Geert & Moffitt, Robert, 2007. "The Econometrics of Data Combination," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 75, Elsevier.
    9. Natalie Kronik & Yuri Kogan & Moran Elishmereni & Karin Halevi-Tobias & Stanimir Vuk-Pavlović & Zvia Agur, 2010. "Predicting Outcomes of Prostate Cancer Immunotherapy by Personalized Mathematical Models," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-8, December.
    10. Joël Mossong & Niel Hens & Mark Jit & Philippe Beutels & Kari Auranen & Rafael Mikolajczyk & Marco Massari & Stefania Salmaso & Gianpaolo Scalia Tomba & Jacco Wallinga & Janneke Heijne & Malgorzata Sa, 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 5(3), pages 1-1, March.
    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. D'Haultfoeuille, Xavier & Gaillac, Christophe & Maurel, Arnaud, 2024. "Linear Regressions with Combined Data," TSE Working Papers 24-1602, Toulouse School of Economics (TSE).
    2. Xavier D'Haultfoeuille & Christophe Gaillac & Arnaud Maurel, 2021. "Rationalizing rational expectations: Characterizations and tests," Quantitative Economics, Econometric Society, vol. 12(3), pages 817-842, July.
    3. X D’Haultfœuille & C Gaillac & A Maurel, 2025. "Partially Linear Models under Data Combination," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 92(1), pages 238-267.
    4. repec:plo:pone00:0045113 is not listed on IDEAS
    5. repec:plo:pone00:0030893 is not listed on IDEAS
    6. repec:plo:pone00:0118457 is not listed on IDEAS
    7. Nathan Kallus, 2022. "What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment," Papers 2205.10327, arXiv.org, revised Nov 2022.
    8. Ichino, Andrea & Favero, Carlo A. & Rustichini, Aldo, 2020. "Restarting the economy while saving lives under Covid-19," CEPR Discussion Papers 14664, C.E.P.R. Discussion Papers.
    9. Marina Antillón & Xiao Li & Lander Willem & Joke Bilcke & RESCEU investigators & Mark Jit & Philippe Beutels, 2023. "The age profile of respiratory syncytial virus burden in preschool children of low- and middle-income countries: A semi-parametric, meta-regression approach," PLOS Medicine, Public Library of Science, vol. 20(7), pages 1-25, July.
    10. Christian Bontemps & Thierry Magnac & Eric Maurin, 2012. "Set Identified Linear Models," Econometrica, Econometric Society, vol. 80(3), pages 1129-1155, May.
    11. M. Hashem Pesaran & Cynthia Fan Yang, 2022. "Matching theory and evidence on Covid‐19 using a stochastic network SIR model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1204-1229, September.
    12. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    13. Kasy, Maximilian, 2011. "A nonparametric test for path dependence in discrete panel data," Economics Letters, Elsevier, vol. 113(2), pages 172-175.
    14. Atı̇la Abdulkadı̇roğlu & Joshua D. Angrist & Yusuke Narita & Parag Pathak, 2022. "Breaking Ties: Regression Discontinuity Design Meets Market Design," Econometrica, Econometric Society, vol. 90(1), pages 117-151, January.
    15. S. M. Niaz Arifin & Christoph Zimmer & Caroline Trotter & Anaïs Colombini & Fati Sidikou & F. Marc LaForce & Ted Cohen & Reza Yaesoubi, 2019. "Cost-Effectiveness of Alternative Uses of Polyvalent Meningococcal Vaccines in Niger: An Agent-Based Transmission Modeling Study," Medical Decision Making, , vol. 39(5), pages 553-567, July.
    16. Mohamed M. Mousa & Fahad Alsharari, 2021. "A Comparative Numerical Study and Stability Analysis for a Fractional-Order SIR Model of Childhood Diseases," Mathematics, MDPI, vol. 9(22), pages 1-12, November.
    17. Bisin, Alberto & Moro, Andrea, 2022. "Spatial‐SIR with network structure and behavior: Lockdown rules and the Lucas critique," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 370-388.
    18. Mirjam Kretzschmar & Rafael T Mikolajczyk, 2009. "Contact Profiles in Eight European Countries and Implications for Modelling the Spread of Airborne Infectious Diseases," PLOS ONE, Public Library of Science, vol. 4(6), pages 1-8, June.
    19. Arai, Yoichi & Otsu, Taisuke & Xu, Mengshan, 2024. "GLS under monotone heteroskedasticity," Journal of Econometrics, Elsevier, vol. 246(1).
    20. Andrei I. Vlad & Alexei A. Romanyukha & Tatiana E. Sannikova, 2024. "Parameter Tuning of Agent-Based Models: Metaheuristic Algorithms," Mathematics, MDPI, vol. 12(14), pages 1-21, July.
    21. Böckerman, Petri & Vainiomäki, Jari, 2013. "Stature and life-time labor market outcomes: Accounting for unobserved differences," Labour Economics, Elsevier, vol. 24(C), pages 86-96.
    22. Pirayesh, Amir & Asadaraghi, Alireza & Mohammadi, Mehrdad & Siadat, Ali & Battaïa, Olga, 2025. "A dynamic optimization model for vaccine allocation with age considerations: A study inspired by the COVID-19 pandemic," International Journal of Production Economics, Elsevier, vol. 280(C).
    23. Ashesh Rambachan & Jonathan Roth, 2020. "Design-Based Uncertainty for Quasi-Experiments," Papers 2008.00602, arXiv.org, revised Oct 2024.

    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:spr:stpapr:v:65:y:2024:i:1:d:10.1007_s00362-022-01375-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.