The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Improving Predictions using Ensemble Bayesian Model Averaging
Open accessAbstract
We present ensemble Bayesian model averaging (EBMA) and illustrate its ability to aid scholars in the social sciences to make more accurate forecasts of future events. In essence, EBMA improves prediction by pooling information from multiple forecast models to generate ensemble predictions similar to a weighted average of component forecasts. The weight assigned to each forecast is calibrated via its performance in some validation period. The aim is not to choose some “best” model, but rather to incorporate the insights and knowledge implicit in various forecasting efforts via statistical postprocessing. After presenting the method, we show that EBMA increases the accuracy of out-of-sample forecasts relative to component models in three applied examples: predicting the occurrence of insurgencies around the Pacific Rim, forecasting vote shares in U.S. presidential elections, and predicting the votes of U.S. Supreme Court Justices.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Ethnicity, Insurgency, and Civil War
FEARON, J. D., & LAITIN, D. D. (2003) · American Political Science Review · doi:10.1017/s0003055403000534 · Cited by 7,366
Causation, Prediction, and Search
Spirtes, P., Glymour, C., & Scheines, R. (2001) · doi:10.7551/mitpress/1754.001.0001 · Cited by 4,521
Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999) · Statistical Science · doi:10.1214/ss/1009212519 · Cited by 4,118 · cited 1×
“EBMA itself is an extension of the Bayesian model averaging (BMA) methodology (cf. Madigan and Raftery 1994; Draper 1995; Raftery 1995; Hoeting et al. 1999; Clyde 2003; Raftery and Zheng 2003; Clyde and George 2004) that has received considerable attention in the field of statistics.”
MethodBates, J. M., & Granger, C. W. J. (1969) · Journal of the Operational Research Society · doi:10.1057/jors.1969.103 · Cited by 3,201
Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
Madigan, D., & Raftery, A. E. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476894 · Cited by 1,205
Dawid, A. P. (1984) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2981683 · Cited by 1,085
Dawid, A. P. (1982) · Journal of the American Statistical Association · doi:10.1080/01621459.1982.10477856 · Cited by 625
Bayesian Modeling of Uncertainty in Ensembles of Climate Models
Smith, R. L., Tebaldi, C., Nychka, D., & Mearns, L. O. (2009) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.0007 · Cited by 225
Forecasting in International Relations: One Quest, Three Approaches
Schneider, G., Gleditsch, N. P., & Carey, S. (2011) · Conflict Management and Peace Science · doi:10.1177/0738894210388079 · Cited by 52
Verification of forecasts expressed in terms of probability
BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078%3c0001:vofeit%3e2.0.co;2 · cited 1×
“Thus, a lower score corresponds to higher forecast accuracy (Brier 1950).”
Background
Cited by
A comprehensive review on ensemble deep learning: Opportunities and challenges
Mohammed, A., & Kora, R. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.01.014 · Cited by 1,127
Muchlinski, D., Siroky, D., He, J., & Kocher, M. (2016) · Political Analysis · doi:10.1093/pan/mpv024 · Cited by 234
ViEWS: A political violence early-warning system
Hegre, H., Allansson, M., Basedau, M., Colaresi, M., Croicu, M., Fjelde, H., Hoyles, F., Hultman, L., Högbladh, S., Jansen, R., et al. (2019) · Journal of Peace Research · doi:10.1177/0022343319823860 · Cited by 173
Combining forecasts: An application to elections
Graefe, A., Armstrong, J. S., Jones, R. J., & Cuzán, A. G. (2014) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2013.02.005 · Cited by 160
Forecasting Civil Wars: Theory and Structure in an Age of “Big Data” and Machine Learning
Blair, R. A., & Sambanis, N. (2020) · Journal of Conflict Resolution · doi:10.1177/0022002720918923 · Cited by 52
Metadata
- Authors
- Montgomery, J. M., Hollenbach, F. M., & Ward, M. D.
- Venue
- Political Analysis
- Publisher
- Cambridge University Press (CUP)
- Published
- 2012
- Volume
- 20
- Issue
- 3
- Pages
- 271-291
- ISSN
- 1047-1987 (ISSN-L)
- 1476-4989
- OpenAlex
- W2319803923
- Cited by
- 158
- References
- 10
- FWCI
- 13.53
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Improving Predictions using Ensemble Bayesian Model Averaging. https://app.cochat.ai/papers/10.1093/pan/mps002
- The paper
- Montgomery, J. M., Hollenbach, F. M., & Ward, M. D. (2012). Improving Predictions using Ensemble Bayesian Model Averaging. Political Analysis. https://doi.org/10.1093/pan/mps002
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
