|
View: |
Part 1: Document Description
|
|
Citation |
|
|---|---|
|
Title: |
Replication data for: Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems |
|
Identification Number: |
doi:10.7910/DVN/EBMA |
|
Distributor: |
Harvard Dataverse |
|
Date of Distribution: |
2014-07-21 |
|
Version: |
1 |
|
Bibliographic Citation: |
Graefe, Andreas, 2014, "Replication data for: Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems", https://doi.org/10.7910/DVN/EBMA, Harvard Dataverse, V1 |
|
Citation |
|
|
Title: |
Replication data for: Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems |
|
Identification Number: |
doi:10.7910/DVN/EBMA |
|
Authoring Entity: |
Graefe, Andreas (LMU Munich) |
|
Date of Production: |
2014 |
|
Distributor: |
Harvard Dataverse |
|
Distributor: |
Harvard Dataverse Network |
|
Date of Deposit: |
2014-07-21 |
|
Date of Distribution: |
2014 |
|
Holdings Information: |
https://doi.org/10.7910/DVN/EBMA |
|
Study Scope |
|
|
Keywords: |
Bayesian Model Averaging, election forecasting |
|
Abstract: |
We compare the accuracy of simple unweighted averages and Ensemble Bayesian Model Averaging (EBMA) to combining forecasts in the social sciences. A review of prior studies from the domain of economic forecasting finds that the simple average was more accurate than EBMA in four out of five studies. On average, the error of EBMA was 5% higher than the error of the simple average. A reanalysis and extension of a published study provides further evidence for US presidential election forecasting. The error of EBMA was 33% higher than the corresponding error of the simple average. Simple averages are easy to describe, easy to understand and thus easy to use. In addition, simple averages provide accurate forecasts in many settings. Researchers who develop new approaches to combining forecasts need to compare the accuracy of their method to this widely established benchmark. Forecasting practitioners should favor simple averages over more complex methods unless there is strong evidence in support of differential weights. |
|
Methodology and Processing |
|
|
Sources Statement |
|
|
Data Access |
|
|
Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
|
Other Study Description Materials |
|
|
Related Publications |
|
|
Citation |
|
|
Title: |
Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems |
|
Bibliographic Citation: |
Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems |
|
Label: |
EBMA.xlsx |
|
Text: | |
|
Notes: |
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet |