Ensemble Predictions of the 2012 US Presidential Election
Bibliographic Data
| ID | 6099029 |
|---|---|
| Authors | Jacob M Montgomery (0000-0001-5632-2437, Washington University in St. Louis), Florian M Hollenbach (0000-0002-9599-556X, Duke University), Michael D Ward (0000-0002-6561-6186, Duke University) |
| Year | 2012 |
| Volume | 45 |
| Issue | 4 |
| Pages | 651-654 |
| Publication date | 2012-10-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | PS Political Science & Politics (JOURNAL) |
| Journal identifiers | ISSN: 1049-0965 • E-ISSN: 1537-5935 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/s1049096512000959 |
| OpenAlex | W2115514302 |
| Language | EN |
| Citations received | 8 |
| References cited | 14 |
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
Data science · Econometrics · Economics · Management science · Mathematical economics · Political science · Politics · Presidential election · Presidential system · Statistical model · Computer Science · Data Analysis with R · Electoral Systems and Political Participation · Forecasting Techniques and Applications · Law · Mathematics · Artificial Intelligence
The 2012 Presidential Election
The Predictive Power of Uniform Swing
The Future of Election Forecasting
Aggregation and Ensembles
Four Suggestions for Making Election Forecasts Better, and Better Known
Density tourism demand forecasting revisited
Opinion Polling and Election Predictions
Predicting Elections
| Citations per year | 0.62 |
|---|---|
| Citation span | 2013 - 2019 (7) |
| Highly cited | No |