IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v37y2021i4p1654-1665.html
   My bibliography  Save this article

Forecasting government support in Irish general elections: Opinion polls and structural models

Author

Listed:
  • Quinlan, Stephen
  • Lewis-Beck, Michael S.

Abstract

Election forecasting is a cottage industry among pollsters, the media, political scientists, and political anoraks. Here, we plow a fresh field in providing a systematic exploration of election forecasting in Ireland. We develop a structural forecast model for predicting incumbent government support in Irish general elections between 1977 and 2020 (the Iowa model). We contrast this structural model with forecasts from opinion polls, the dominant means of predicting Ireland’s elections to date. Our results show that with appropriate lead-in time, structural models perform similarly to opinion polls in predicting government support when the dependent variable is vote share. Most importantly, however, the Iowa model is superior to opinion polls in predicting government seat share, the ultimate decider of government fate in parliamentary systems, and especially significant in single transferable vote (STV) systems where vote and seat shares are not always in sync. Our results provide cumulative evidence of the potency of structural electoral forecast models globally, with the takeaway that the Iowa model estimating seat share outpaces other prediction approaches in anticipating government performance in Irish general elections.

Suggested Citation

  • Quinlan, Stephen & Lewis-Beck, Michael S., 2021. "Forecasting government support in Irish general elections: Opinion polls and structural models," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1654-1665.
    • Handle: RePEc:eee:intfor:v:37:y:2021:i:4:p:1654-1665
    DOI: 10.1016/j.ijforecast.2021.03.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207021000571
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2021.03.006?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. Lewis-Beck, Michael S. & Tien, Charles, 2012. "Japanese election forecasting: Classic tests of a hard case," International Journal of Forecasting, Elsevier, vol. 28(4), pages 797-803.
    2. Bellucci, Paolo, 2010. "Election cycles and electoral forecasting in Italy, 1994-2008," International Journal of Forecasting, Elsevier, vol. 26(1), pages 54-67, January.
    3. Aichholzer, Julian & Willmann, Johanna, 2014. "Forecasting Austrian national elections: The Grand Coalition model," International Journal of Forecasting, Elsevier, vol. 30(1), pages 55-64.
    4. Fair, Ray C, 1978. "The Effect of Economic Events on Votes for President," The Review of Economics and Statistics, MIT Press, vol. 60(2), pages 159-173, May.
    5. Nadeau, Richard & Lewis-Beck, Michael S., 2020. "Election forecasts: Cracking the Danish case," International Journal of Forecasting, Elsevier, vol. 36(3), pages 892-898.
    6. Kou, S. G. & Sobel, Michael E., 2004. "Forecasting the Vote: A Theoretical Comparison of Election Markets and Public Opinion Polls," Political Analysis, Cambridge University Press, vol. 12(3), pages 277-295, July.
    7. Murr, Andreas E. & Stegmaier, Mary & Lewis-Beck, Michael S., 2021. "Vote Expectations Versus Vote Intentions: Rival Forecasting Strategies," British Journal of Political Science, Cambridge University Press, vol. 51(1), pages 60-67, January.
    8. Dassonneville, Ruth & Hooghe, Marc, 2012. "Election forecasting under opaque conditions: A model for Francophone Belgium, 1981–2010," International Journal of Forecasting, Elsevier, vol. 28(4), pages 777-788.
    9. Stoetzer, Lukas F. & Neunhoeffer, Marcel & Gschwend, Thomas & Munzert, Simon & Sternberg, Sebastian, 2019. "Forecasting Elections in Multiparty Systems: A Bayesian Approach Combining Polls and Fundamentals," Political Analysis, Cambridge University Press, vol. 27(2), pages 255-262, April.
    10. Michael Lewis-Beck & Mary Stegmaier, 2013. "The VP-function revisited: a survey of the literature on vote and popularity functions after over 40 years," Public Choice, Springer, vol. 157(3), pages 367-385, December.
    11. Arnesen, Sveinung, 2012. "Forecasting Norwegian elections: Out of work and out of office," International Journal of Forecasting, Elsevier, vol. 28(4), pages 789-796.
    12. Norpoth, Helmut & Gschwend, Thomas, 2010. "The chancellor model: Forecasting German elections," International Journal of Forecasting, Elsevier, vol. 26(1), pages 42-53, January.
    13. 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.
    14. Lewis-Beck, Michael S. & Skalaban, Andrew, 1989. "Citizen Forecasting: Can Voters See into the Future?," British Journal of Political Science, Cambridge University Press, vol. 19(1), pages 146-153, January.
    15. Harrison, Michael J & Marsh, Michael, 1998. "A Re-examination of an Irish Popularity Function," Public Choice, Springer, vol. 94(3-4), pages 367-383, March.
    16. Michael Harrison & Michael Marsh, 1998. "A re-examination of an Irish popularity function," Public Choice, Springer, vol. 94(3), pages 367-383, March.
    17. Bornier Jean Magnan de & Norpoth H. & Lewis-Beck M.S. & Lafay J.D., 1991. "Economics and Politics The calculus of support," Journal des Economistes et des Etudes Humaines, De Gruyter, vol. 2(4), pages 579-581, December.
    18. Abramowitz, Alan I., 2008. "It's about time: Forecasting the 2008 presidential election with the time-for-change model," International Journal of Forecasting, Elsevier, vol. 24(2), pages 209-217.
    19. 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.
    20. Paul W. Rhode & Koleman S. Strumpf, 2004. "Historical Presidential Betting Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 127-141, Spring.
    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. Reinhard Neck & Friedrich Schneider, 2024. "The popularity function: a spurious regression? The case of Austria," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 31(1), pages 298-329, February.
    2. Nadeau, Richard & Lewis-Beck, Michael S., 2020. "Election forecasts: Cracking the Danish case," International Journal of Forecasting, Elsevier, vol. 36(3), pages 892-898.
    3. Aichholzer, Julian & Willmann, Johanna, 2014. "Forecasting Austrian national elections: The Grand Coalition model," International Journal of Forecasting, Elsevier, vol. 30(1), pages 55-64.
    4. Wiesen, Taylor, 2023. "Aggregate earnings and market expectations in United States presidential election prediction markets," Advances in accounting, Elsevier, vol. 60(C).
    5. Gebhard Kirchgässner, 2016. "Voting and Popularity," CREMA Working Paper Series 2016-08, Center for Research in Economics, Management and the Arts (CREMA).
    6. Kang, Seungwoo & Oh, Hee-Seok, 2024. "Forecasting South Korea’s presidential election via multiparty dynamic Bayesian modeling," International Journal of Forecasting, Elsevier, vol. 40(1), pages 124-141.
    7. Antoine Auberger, 2021. "Vote, popularity, economic conditions and French legislative elections [Vote, popularité, conditions économiques et élections législatives françaises]," Working Papers hal-03480853, HAL.
    8. Franch, Fabio, 2021. "Political preferences nowcasting with factor analysis and internet data: The 2012 and 2016 US presidential elections," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    9. Antoine Auberger, 2020. "The impact of economic and political factors on popularity for France (1981- 2017)," Working Papers hal-02501677, HAL.
    10. repec:ulb:ulbcvp:p0015 is not listed on IDEAS
    11. Bunker, Kenneth, 2020. "A two-stage model to forecast elections in new democracies," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1407-1419.
    12. Renaud Foucart & Marjorie Gassner & Emilie Van Haute, 2012. "Une typologie des résultats électoraux basée sur le comportement des électeurs volatiles en Belgique," CEVIPOL Working Papers 3, ULB -- Universite Libre de Bruxelles.
    13. Rothschild, David, 2015. "Combining forecasts for elections: Accurate, relevant, and timely," International Journal of Forecasting, Elsevier, vol. 31(3), pages 952-964.
    14. Hanretty, Chris, 2021. "Forecasting multiparty by-elections using Dirichlet regression," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1666-1676.
    15. repec:cup:judgdm:v:15:y:2020:i:5:p:863-880 is not listed on IDEAS
    16. Fidrmuc, Jan, 2000. "Political support for reforms: Economics of voting in transition countries," European Economic Review, Elsevier, vol. 44(8), pages 1491-1513, August.
    17. Dai, Min & Jia, Yanwei & Kou, Steven, 2021. "The wisdom of the crowd and prediction markets," Journal of Econometrics, Elsevier, vol. 222(1), pages 561-578.
    18. E Goulas & C Kallandranis & A Zervoyianni, 2019. "Voting Behaviour and the Economy: Evidence from Greece," Economic Issues Journal Articles, Economic Issues, vol. 24(1), pages 35-58, March.
    19. Alberto Alesina & Roberto Perotti & José Tavares, 1998. "The Political Economy of Fiscal Adjustments," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 29(1), pages 197-266.
    20. Dassonneville, Ruth & Hooghe, Marc, 2012. "Election forecasting under opaque conditions: A model for Francophone Belgium, 1981–2010," International Journal of Forecasting, Elsevier, vol. 28(4), pages 777-788.
    21. Luís Aguiar-Conraria & Pedro C. Magalhães, 2018. "Procedural Fairness, the Economy, and Support for Political Authorities (Forthcoming at Political Psychology (submitted pre-print version))," NIPE Working Papers 05/2018, NIPE - Universidade do Minho.
    22. 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.

    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:37:y:2021:i:4:p:1654-1665. 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.