Skip to main content
×
Home

Improving Predictions using Ensemble Bayesian Model Averaging

  • Jacob M. Montgomery (a1), Florian M. Hollenbach (a2) and Michael D. Ward (a2)
Abstract

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.

Copyright
Corresponding author
e-mail: michael.d.ward@duke.edu (corresponding author)
Footnotes
Hide All

Authors' note: For generously sharing their data and models with us, we thank Alan Abramowitz, James Campbell, Robert Erikson, Ray Fair, Douglas Hibbs, Michael Lewis-Beck, Andrew D. Martin, Kevin Quinn, Stephen Shellman, Charles Tien, and Christopher Wlezien. We especially want to thank Adrian Raftery and Brendan Nyhan for their encouragement and feedback as this project evolved. The editor and the reviewers of Political Analysis provided especially salient suggestions that substantially improved our research.

Footnotes
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×
MathJax

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 36 *
Loading metrics...

Abstract views

Total abstract views: 159 *
Loading metrics...

* Views captured on Cambridge Core between 4th January 2017 - 20th November 2017. This data will be updated every 24 hours.