IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v36y2020i2p399-413.html
   My bibliography  Save this article

Model-based pre-election polling for national and sub-national outcomes in the US and UK

Author

Listed:
  • Lauderdale, Benjamin E.
  • Bailey, Delia
  • Blumenau, Jack
  • Rivers, Douglas

Abstract

We describe a strategy for applying multilevel regression and post-stratification (MRP) methods to pre-election polling. Using a combination of contemporaneous polling, census data, past election polling, past election results, and other sources of information, we are able to construct probabilistic, internally consistent estimates of national votes and the sub-national electoral districts that determine seats or electoral votes in many electoral systems. We report on the performance of the general framework in three applications that were conducted and released publicly in advance of the 2016 UK Referendum on EU Membership, the 2016 US Presidential Election, and the 2017 UK General Election.

Suggested Citation

  • Lauderdale, Benjamin E. & Bailey, Delia & Blumenau, Jack & Rivers, Douglas, 2020. "Model-based pre-election polling for national and sub-national outcomes in the US and UK," International Journal of Forecasting, Elsevier, vol. 36(2), pages 399-413.
    • Handle: RePEc:eee:intfor:v:36:y:2020:i:2:p:399-413
    DOI: 10.1016/j.ijforecast.2019.05.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016920701930189X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2019.05.012?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. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Park, David K. & Gelman, Andrew & Bafumi, Joseph, 2004. "Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls," Political Analysis, Cambridge University Press, vol. 12(4), pages 375-385.
    3. Will Jennings & Christopher Wlezien, 2018. "Election polling errors across time and space," Nature Human Behaviour, Nature, vol. 2(4), pages 276-283, April.
    4. Selb, Peter & Munzert, Simon, 2011. "Estimating Constituency Preferences from Sparse Survey Data Using Auxiliary Geographic Information," Political Analysis, Cambridge University Press, vol. 19(4), pages 455-470.
    5. Jackman, Simon & Spahn, Bradley, 2019. "Why Does the American National Election Study Overestimate Voter Turnout?," Political Analysis, Cambridge University Press, vol. 27(2), pages 193-207, April.
    6. Drew A. Linzer, 2013. "Dynamic Bayesian Forecasting of Presidential Elections in the States," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 124-134, March.
    7. Hanretty, Chris & Lauderdale, Benjamin E. & Vivyan, Nick, 2018. "Comparing Strategies for Estimating Constituency Opinion from National Survey Samples," Political Science Research and Methods, Cambridge University Press, vol. 6(3), pages 571-591, July.
    8. Lauderdale, Benjamin E. & Linzer, Drew, 2015. "Under-performing, over-performing, or just performing? The limitations of fundamentals-based presidential election forecasting," International Journal of Forecasting, Elsevier, vol. 31(3), pages 965-979.
    9. Lucas Leemann & Fabio Wasserfallen, 2017. "Extending the Use and Prediction Precision of Subnational Public Opinion Estimation," American Journal of Political Science, John Wiley & Sons, vol. 61(4), pages 1003-1022, October.
    10. Jeffrey R. Lax & Justin H. Phillips, 2009. "How Should We Estimate Public Opinion in The States?," American Journal of Political Science, John Wiley & Sons, vol. 53(1), pages 107-121, January.
    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. Hanretty, Chris, 2021. "Forecasting multiparty by-elections using Dirichlet regression," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1666-1676.

    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. Christopher Claassen & Richard Traunmüller, 2020. "Improving and Validating Survey Estimates of Religious Demography Using Bayesian Multilevel Models and Poststratification," Sociological Methods & Research, , vol. 49(3), pages 603-636, August.
    2. Munzert, Simon, 2017. "Forecasting elections at the constituency level: A correction–combination procedure," International Journal of Forecasting, Elsevier, vol. 33(2), pages 467-481.
    3. Marina Christofoletti & Tânia R. B. Benedetti & Felipe G. Mendes & Humberto M. Carvalho, 2021. "Using Multilevel Regression and Poststratification to Estimate Physical Activity Levels from Health Surveys," IJERPH, MDPI, vol. 18(14), pages 1-16, July.
    4. Roberto Cerina & Raymond Duch, 2021. "Polling India via regression and post-stratification of non-probability online samples," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-34, November.
    5. Montalvo, José G. & Papaspiliopoulos, Omiros & Stumpf-Fétizon, Timothée, 2019. "Bayesian forecasting of electoral outcomes with new parties’ competition," European Journal of Political Economy, Elsevier, vol. 59(C), pages 52-70.
    6. José Garcia Montalvo & Omiros Papaspiliopoulos & Timothée Stumpf-Fétizon, 2018. "Bayesian forecasting of electoral outcomes with new parties' competition," Economics Working Papers 1624, Department of Economics and Business, Universitat Pompeu Fabra.
    7. José García-Montalvo & Omiros Papaspiliopoulos & Timothée Stumpf-Fétizon, 2018. "Bayesian Forecasting of Electoral Outcomes with new Parties' Competition," Working Papers 1065, Barcelona School of Economics.
    8. Isakov, Michael & Kuriwaki, Shiro, 2020. "Towards Principled Unskewing: Viewing 2020 Election Polls Through a Corrective Lens from 2016," OSF Preprints 29pvm, Center for Open Science.
    9. Bunker, Kenneth, 2020. "A two-stage model to forecast elections in new democracies," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1407-1419.
    10. Wang, Wei & Rothschild, David & Goel, Sharad & Gelman, Andrew, 2015. "Forecasting elections with non-representative polls," International Journal of Forecasting, Elsevier, vol. 31(3), pages 980-991.
    11. Jonathan Gellar & Sarah Hughes & Constance Delannoy & Erin Lipman & Shirley Jeoffreys-Leach & Bobby Berkowitz & Grant J. Robertson, "undated". "Calibrated Multilevel Regression with Poststratifiction for the Analysis of SMS Survey Data," Mathematica Policy Research Reports c71d456bbf9f4026988e1a810, Mathematica Policy Research.
    12. Facchini, Giovanni & Hatton, Timothy J. & Steinhardt, Max F., 2024. "Opening Heaven’s Door: Public Opinion and Congressional Votes on the 1965 Immigration Act," The Journal of Economic History, Cambridge University Press, vol. 84(1), pages 232-270, March.
    13. Caldarulo, Mattia & Mossberger, Karen & Howell, Anthony, 2023. "Community-wide broadband adoption and student academic achievement," Telecommunications Policy, Elsevier, vol. 47(1).
    14. Cerina, Roberto & Duch, Raymond, 2020. "Measuring public opinion via digital footprints," International Journal of Forecasting, Elsevier, vol. 36(3), pages 987-1002.
    15. Bruch, Christian & Felderer, Barbara, 2024. "An Approximation of Joint Distributions of Weighting Variables Using a Pseudo Population Approach," OSF Preprints pg2wt, Center for Open Science.
    16. Easaw, Joshy & Fang, Yongmei & Heravi, Saeed, 2021. "Using Polls to Forecast Popular Vote Share for US Presidential Elections 2016 and 2020: An Optimal Forecast Combination Based on Ensemble Empirical Model," Cardiff Economics Working Papers E2021/34, Cardiff University, Cardiff Business School, Economics Section.
    17. Liu, Yezheng & Ye, Chang & Sun, Jianshan & Jiang, Yuanchun & Wang, Hai, 2021. "Modeling undecided voters to forecast elections: From bandwagon behavior and the spiral of silence perspective," International Journal of Forecasting, Elsevier, vol. 37(2), pages 461-483.
    18. Laura C. Dawkins & Daniel B. Williamson & Stewart W. Barr & Sally R. Lampkin, 2020. "‘What drives commuter behaviour?': a Bayesian clustering approach for understanding opposing behaviours in social surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 251-280, January.
    19. Levene, Mark & Fenner, Trevor, 2021. "A stochastic differential equation approach to the analysis of the 2017 and 2019 UK general election polls," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1227-1234.
    20. Francis,David C. & Kubinec ,Robert, 2022. "Beyond Political Connections : A Measurement Model Approach to Estimating Firm-levelPolitical Influence in 41 Economies," Policy Research Working Paper Series 10119, The World Bank.

    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:intfor:v:36:y:2020:i:2:p:399-413. 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/ijforecast .

    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.