IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v24y2008i2p209-217.html
   My bibliography  Save this article

It's about time: Forecasting the 2008 presidential election with the time-for-change model

Author

Listed:
  • Abramowitz, Alan I.

Abstract

The popular vote for president can be predicted accurately before the national nominating conventions based on three factors: the incumbent president's approval rating at mid-year, the growth rate of the economy during the first half of the election year, and the length of time that the president's party has controlled the White House. Regardless of who wins the Democratic and Republican nominations, 2008 will be a time-for-change presidential election. Based on President Bush's approval rating in June of 2007, the recent growth rate of the economy, and the fact that the Republican Party will have controlled the White House for eight years, the Democratic nominee would be predicted to win the national popular vote by a comfortable margin. For the Republican nominee to have a reasonable chance of winning the 2008 presidential election, there would have to be a dramatic improvement in President Bush's approval rating during the next 12Â months.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:intfor:v:24:y:2008:i:2:p:209-217
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169-2070(08)00022-8
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. 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.
    2. Douglas Hibbs, 2000. "Bread and Peace Voting in U.S. Presidential Elections," Public Choice, Springer, vol. 104(1), pages 149-180, July.
    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. 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.
    2. Montgomery, Jacob M. & Hollenbach, Florian M. & Ward, Michael D., 2015. "Calibrating ensemble forecasting models with sparse data in the social sciences," International Journal of Forecasting, Elsevier, vol. 31(3), pages 930-942.
    3. 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.
    4. Turgeon, Mathieu & Rennó, Lucio, 2012. "Forecasting Brazilian presidential elections: Solving the N problem," International Journal of Forecasting, Elsevier, vol. 28(4), pages 804-812.
    5. Montone, Maurizio, 2022. "Does the U.S. president affect the stock market?," Journal of Financial Markets, Elsevier, vol. 61(C).
    6. 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.
    7. Rothschild, David, 2015. "Combining forecasts for elections: Accurate, relevant, and timely," International Journal of Forecasting, Elsevier, vol. 31(3), pages 952-964.
    8. Norpoth, Helmut & Gschwend, Thomas, 2010. "The chancellor model: Forecasting German elections," International Journal of Forecasting, Elsevier, vol. 26(1), pages 42-53, January.
    9. 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).
    10. Kurrild-Klitgaard, Peter, 2012. "Too close to call: Growth and the cost of ruling in US presidential elections, with an application to the 2012 election," MPRA Paper 42464, University Library of Munich, Germany.
    11. 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.
    12. Ji Won Jung & Jinhwan Oh, 2020. "Determinants of presidential approval ratings: Cross-country analyses with reference to Latin America," International Area Studies Review, Center for International Area Studies, Hankuk University of Foreign Studies, vol. 23(3), pages 251-267, September.
    13. Wiesen, Taylor, 2023. "Aggregate earnings and market expectations in United States presidential election prediction markets," Advances in accounting, Elsevier, vol. 60(C).
    14. Rajiv Sethi & Julie Seager & Emily Cai & Daniel M. Benjamin & Fred Morstatter, 2021. "Models, Markets, and the Forecasting of Elections," Papers 2102.04936, arXiv.org, revised May 2021.
    15. Antoine Auberger, 2012. "Voting and economic factors in French elections for the European Parliament," Public Choice, Springer, vol. 153(3), pages 329-340, December.
    16. John Sides & Michael Tesler & Lynn Vavreck, 2016. "The Electoral Landscape of 2016," The ANNALS of the American Academy of Political and Social Science, , vol. 667(1), pages 50-71, September.

    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. Sinha, Pankaj & Srinivas, Sandeep & Paul, Anik & Chaudhari, Gunjan, 2016. "Forecasting 2016 US Presidential Elections Using Factor Analysis and Regression Model," MPRA Paper 74618, University Library of Munich, Germany, revised 17 Oct 2016.
    2. 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.
    3. Souren Soumbatiants & Henry Chappell & Eric Johnson, 2006. "Using state polls to forecast U.S. Presidential election outcomes," Public Choice, Springer, vol. 127(1), pages 207-223, April.
    4. Pankaj Sinha & Ashok K. Bansal, 2008. "Hierarchical Bayes Prediction for the 2008 US Presidential Election," Journal of Prediction Markets, University of Buckingham Press, vol. 2(3), pages 47-59, December.
    5. repec:cup:judgdm:v:15:y:2020:i:5:p:863-880 is not listed on IDEAS
    6. Sinha, Pankaj & Verma, Aniket & Shah, Purav & Singh, Jahnavi & Panwar, Utkarsh, 2020. "Prediction for the 2020 United States Presidential Election using Machine Learning Algorithm: Lasso Regression," MPRA Paper 103889, University Library of Munich, Germany, revised 31 Oct 2020.
    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. Pankaj Sinha & Aastha Sharma & Harsh Vardhan Singh, 2012. "Prediction For The 2012 United States Presidential Election Using Multiple Regression Model," Journal of Prediction Markets, University of Buckingham Press, vol. 6(2), pages 77-97.
    9. Kurrild-Klitgaard, Peter, 2019. "Var det fortsat ”the economy, stupid!” i 2016 og 2018? [Was it still "the economy, stupid!" in 2016 and 2018?]," MPRA Paper 97297, University Library of Munich, Germany.
    10. Sinha, Pankaj & Verma, Aniket & Shah, Purav & Singh, Jahnavi & Panwar, Utkarsh, 2020. "Prediction for the 2020 United States Presidential Election using Linear Regression Model," MPRA Paper 103890, University Library of Munich, Germany, revised 20 Oct 2020.
    11. Hibbs Jr., Douglas A., 2004. "Voting and the Macroeconomy," Working Papers in Economics 144, University of Gothenburg, Department of Economics, revised 08 Apr 2006.
    12. Sinha, Pankaj & Thomas, Ashley Rose & Ranjan, Varun, 2012. "Forecasting 2012 United States Presidential election using Factor Analysis, Logit and Probit Models," MPRA Paper 42062, University Library of Munich, Germany.
    13. Sinha, Pankaj & verma, Kaushal & Biswas, Sumana & Tyagi, Shashank & Gogia, Shaily & Singh, Aakhyat & Kumar, Amit, 2024. "Modeling and forecasting US presidential election 2024," MPRA Paper 122319, University Library of Munich, Germany, revised 08 Oct 2024.
    14. Hibbs, Douglas A., 2007. "The Economy, the War in Iraq and the 2004 Presidential Election," MPRA Paper 15910, University Library of Munich, Germany.
    15. Hibbs, Douglas A, Jr, 2000. "Bread and Peace Voting in U.S. Presidential Elections," Public Choice, Springer, vol. 104(1-2), pages 149-180, July.
    16. Jeroen Klomp, 2020. "Election or Disaster Support?," Journal of Development Studies, Taylor & Francis Journals, vol. 56(1), pages 205-220, January.
    17. Janet Pack, 1987. "The political policy cycle: Presidential effort vs. presidential control," Public Choice, Springer, vol. 54(3), pages 231-259, August.
    18. Boryana Dimitrova, 2000. "An Empirical Model of Voting Behavior in the Bulgarian Parliamentary Elections of 1994," The American Economist, Sage Publications, vol. 44(2), pages 71-77, October.
    19. Paola Assael & Felipe Larraín, 1994. "El Ciclo Político-económico: Teoría, Evidencia y Extensión para una Economía Abierta," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 31(92), pages 87-114.
    20. Erik Snowberg & Justin Wolfers & Eric Zitzewitz, 2007. "Partisan Impacts on the Economy: Evidence from Prediction Markets and Close Elections," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(2), pages 807-829.
    21. Cáceres, Neila & Malone, Samuel W., 2015. "Optimal Weather Conditions, Economic Growth, and Political Transitions," World Development, Elsevier, vol. 66(C), pages 16-30.

    More about this item

    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:intfor:v:24:y:2008:i:2:p:209-217. 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.