IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v33y2017i2p467-481.html
   My bibliography  Save this article

Forecasting elections at the constituency level: A correction–combination procedure

Author

Listed:
  • Munzert, Simon

Abstract

Scholarly efforts to forecast parliamentary elections have targeted the national level predominantly, disregarding the outcomes of constituency races. In doing so, they have frequently failed to account for systematic bias in the seats–votes curve, and been unable to provide candidates and campaign strategists with constituency-level information. On the other hand, existing accounts of constituency-level election forecasting suffer from data sparsity, leading to a lack of precision. This paper proposes a correction–combination procedure that allows for the correction of individual constituency-level forecast models for election-invariant bias, then combines these models based on their past performances. I demonstrate the use of this procedure through out-of-sample forecasts of 299 district races at the 2013 German federal election.

Suggested Citation

  • Munzert, Simon, 2017. "Forecasting elections at the constituency level: A correction–combination procedure," International Journal of Forecasting, Elsevier, vol. 33(2), pages 467-481.
    • Handle: RePEc:eee:intfor:v:33:y:2017:i:2:p:467-481
    DOI: 10.1016/j.ijforecast.2016.12.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207017300031
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2016.12.001?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. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    2. Montgomery, Jacob M. & Hollenbach, Florian M. & Ward, Michael D., 2012. "Improving Predictions using Ensemble Bayesian Model Averaging," Political Analysis, Cambridge University Press, vol. 20(3), pages 271-291, July.
    3. Rothschild, David, 2015. "Combining forecasts for elections: Accurate, relevant, and timely," International Journal of Forecasting, Elsevier, vol. 31(3), pages 952-964.
    4. 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.
    5. Lodge, Milton & Steenbergen, Marco R. & Brau, Shawn, 1995. "The Responsive Voter: Campaign Information and the Dynamics of Candidate Evaluation," American Political Science Review, Cambridge University Press, vol. 89(2), pages 309-326, June.
    6. 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.
    7. 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.
    8. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    9. Issler, João Victor & Rodrigues, Claudia & Burjack, Rafael, 2014. "Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 310-335.
    10. 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.
    11. Gelman, Andrew & King, Gary, 1993. "Why Are American Presidential Election Campaign Polls So Variable When Votes Are So Predictable?," British Journal of Political Science, Cambridge University Press, vol. 23(4), pages 409-451, October.
    12. Jackman, Simon, 1994. "Measuring Electoral Bias: Australia, 1949–93," British Journal of Political Science, Cambridge University Press, vol. 24(3), pages 319-357, July.
    13. Tufte, Edward R., 1973. "The Relationship between Seats and Votes in Two-Party Systems," American Political Science Review, Cambridge University Press, vol. 67(2), pages 540-554, June.
    14. Graefe, Andreas & Armstrong, J. Scott & Jones, Randall J. & Cuzán, Alfred G., 2014. "Combining forecasts: An application to elections," International Journal of Forecasting, Elsevier, vol. 30(1), pages 43-54.
    15. Magalhães, Pedro C. & Aguiar-Conraria, Luís & Lewis-Beck, Michael S., 2012. "Forecasting Spanish elections," International Journal of Forecasting, Elsevier, vol. 28(4), pages 769-776.
    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. Khan, Urmee & Lieli, Robert P., 2018. "Information flow between prediction markets, polls and media: Evidence from the 2008 presidential primaries," International Journal of Forecasting, Elsevier, vol. 34(4), pages 696-710.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. repec:cup:judgdm:v:15:y:2020:i:5:p:863-880 is not listed on IDEAS
    7. Andrew Gelman & Jessica Hullman & Christopher Wlezien & George Elliott Morris, 2020. "Information, incentives, and goals in election forecasts," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(5), pages 863-880, September.
    8. Bunker, Kenneth, 2020. "A two-stage model to forecast elections in new democracies," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1407-1419.
    9. Rothschild, David, 2015. "Combining forecasts for elections: Accurate, relevant, and timely," International Journal of Forecasting, Elsevier, vol. 31(3), pages 952-964.
    10. Graefe, Andreas, 2019. "Accuracy of German federal election forecasts, 2013 & 2017," International Journal of Forecasting, Elsevier, vol. 35(3), pages 868-877.
    11. Graefe, Andreas & Küchenhoff, Helmut & Stierle, Veronika & Riedl, Bernhard, 2015. "Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems," International Journal of Forecasting, Elsevier, vol. 31(3), pages 943-951.
    12. 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.
    13. Fronzetti Colladon, Andrea, 2020. "Forecasting election results by studying brand importance in online news," International Journal of Forecasting, Elsevier, vol. 36(2), pages 414-427.
    14. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    15. 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.
    16. 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.
    17. Satopää, Ville A. & Baron, Jonathan & Foster, Dean P. & Mellers, Barbara A. & Tetlock, Philip E. & Ungar, Lyle H., 2014. "Combining multiple probability predictions using a simple logit model," International Journal of Forecasting, Elsevier, vol. 30(2), pages 344-356.
    18. Andreas Graefe & Kesten C Green & J Scott Armstrong, 2019. "Accuracy gains from conservative forecasting: Tests using variations of 19 econometric models to predict 154 elections in 10 countries," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-14, January.
    19. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    20. Wang, Samuel S.-H., 2015. "Origins of Presidential poll aggregation: A perspective from 2004 to 2012," International Journal of Forecasting, Elsevier, vol. 31(3), pages 898-909.
    21. Nadeau, Richard & Lewis-Beck, Michael S., 2020. "Election forecasts: Cracking the Danish case," International Journal of Forecasting, Elsevier, vol. 36(3), pages 892-898.

    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:33:y:2017:i:2:p:467-481. 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.