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  • Cited by 4
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    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Northcott, Robert 2015. Opinion Polling and Election Predictions. Philosophy of Science, Vol. 82, Issue. 5, p. 1260.


    Pasek, J. 2015. Predicting Elections: Considering Tools to Pool the Polls. Public Opinion Quarterly, Vol. 79, Issue. 2, p. 594.


    Gneiting, Tilmann and Katzfuss, Matthias 2014. Probabilistic Forecasting. Annual Review of Statistics and Its Application, Vol. 1, Issue. 1, p. 125.


    Montgomery, Jacob M. Hollenbach, Florian M. and Ward, Michael D. 2013. AGGREGATION AND ENSEMBLES: PRINCIPLED COMBINATIONS OF DATA. PS: Political Science & Politics, Vol. 46, Issue. 01, p. 43.


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Ensemble Predictions of the 2012 US Presidential Election

  • Jacob M. Montgomery (a1), Florian M. Hollenbach (a2) and Michael D. Ward (a3)
  • DOI: http://dx.doi.org/10.1017/S1049096512000959
  • Published online: 01 September 2012
Abstract

For more than two decades, political scientists have created statistical models aimed at generating out-of-sample predictions of presidential elections. In 2004 and 2008, PS: Political Science and Politics published symposia of the various forecasting models prior to Election Day. This exercise serves to validate models based on accuracy by garnering additional support for those that most accurately foretell the ultimate election outcome. Implicitly, these symposia assert that accurate models best capture the essential contexts and determinants of elections. In part, therefore, this exercise aims to develop the “best” model of the underlying data generating process. Scholars comparatively evaluate their models by setting their predictions against electoral results while also giving some attention to the models' inherent plausibility, parsimony, and beauty.

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This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

L. M. Bartels , 1997. “Specification Uncertainty and Model Averaging.” American Journal of Political Science 41 (2): 641–74.

V. J. Berrocal , A. E. Raftery, T. Gneiting, and R. C. Steed. 2010. “Probabilistic Weather Forecasting for Winter Road Maintenance.” Journal of the American Statistical Association 105 (490): 522–37.

W. A. Brock , S. N. Durlauf, and K. D. West. 2007. “Model Uncertainty and Policy Evaluation: Some Theory and Empirics.” Journal of Econometrics 136 (2): 629–64.

R. M. Chmielecki , and A. E. Raftery. 2010. “Probabilistic Visibility Forecasting Using Bayesian Model Averaging.” Monthly Weather Review 139 (5): 1626–36.

M. Feldkircher 2012. “Forecast Combination and Bayesian Model Averaging: A Prior Sensitivity Analysis.” Journal of Forecasting 31 (4): 361–76.

C. Fraley , A. E. Raftery, and T. Gneiting. 2010. “Calibrating Multimodel Forecast Ensembles with Exchangeable and Missing Members Using Bayesian Model Averaging.” Monthly Weather Review 138 (1): 190202.

J. M. Montgomery , F. M. Hollenbach, and M. D. Ward. 2012a. “Improving Predictions Using Ensemble Bayesian Model Averaging.” Political Analysis 20 (3): 271–91.

J. M. Montgomery , and B. Nyhan. 2010. “Bayesian Model Averaging: Theoretical Developments and Practical Applications.” Political Analysis 18 (2): 245–70.

A. E. Raftery , T. Gneiting, F. Balabdaoui, and M. Polakowski. 2005. “Using Bayesian Model Averaging to Calibrate Forecast Ensembles.” Monthly Weather Review 133 (5): 1155–74.

J. H. Wright 2008. “Bayesian Model Averaging and Exchange Rate Forecasts.” Journal of Econometrics 146 (2): 329–41.

J. H. Wright 2009. “Forecasting US Inflation by Bayesian Model Averaging.” Journal of Forecasting 28 (2): 131–44.

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PS: Political Science & Politics
  • ISSN: 1049-0965
  • EISSN: 1537-5935
  • URL: /core/journals/ps-political-science-and-politics
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