1 to 8 of 8 Results
Mar 2, 2016
Graefe, Andreas, 2016, "Replication Data for: Forecasting proportional representation elections from non-representative expectation surveys", https://doi.org/10.7910/DVN/25XOBR, Harvard Dataverse, V1, UNF:6:ikzdIueMbN5rqmg0kry9Ow== [fileUNF]
This study tests non-representative expectation surveys as a method for forecasting elections. For dichotomous forecasts of the 2013 German election (e.g., who will be chancellor, which parties will enter parliament), two non-representative citizen samples performed equally well than a benchmark group of experts. For vote-share forecasts, the sampl... |
Jan 2, 2015
Graefe, Andreas, 2015, "Replication data for: German election forecasting: Comparing and combining methods for 2013", https://doi.org/10.7910/DVN/GERMANPOLLYVOTE2013, Harvard Dataverse, V1
The present study reviews the accuracy of four methods for forecasting the 2013 German election: polls, prediction markets, expert judgment, and quantitative models. On average, across the two months prior to the election, polls were most accurate, with a mean absolute error of 1.4 percentage points, followed by quantitative models (1.6), expert ju... |
Dec 1, 2014
Graefe, Andreas, 2014, "Replication data for: Accuracy gains of adding vote expectation surveys to a combined forecast of US presidential election outcomes", https://doi.org/10.7910/DVN/27967, Harvard Dataverse, V1
In averaging forecasts within and across four component methods (i.e., polls, prediction markets, expert judgment, and quantitative models), the combined PollyVote provided highly accurate predictions for the US presidential elections from 1992 to 2012. This research note shows that the PollyVote would have also outperformed vote expectation survey... |
Jul 20, 2014
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
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 th... |
Jun 15, 2014
Graefe, Andreas, 2014, "Replication data for: Accuracy of vote expectation surveys in forecasting elections", https://doi.org/10.7910/DVN/VOTEEXPECTATIONSURVEYS, Harvard Dataverse, V1
Simple surveys that ask people who they expect to win are among the most accurate methods for forecasting U.S. presidential elections. The majority of respondents correctly predicted the election winner in 193 (89%) of 217 surveys conducted from 1932 to 2012. Across the last 100 days prior to the seven elections from 1988 to 2012, vote expectation... |
Dec 4, 2013
Graefe, Andreas, 2013, "Replication data for: Combining forecasts: An application to elections", https://doi.org/10.7910/DVN/23184, Harvard Dataverse, V3
We summarize the literature on the effectiveness of combining forecasts by assessing the conditions under which combining is most valuable. Using data on the six US presidential elections from 1992 to 2012, we report the reductions in error obtained by averaging forecasts within and across four election forecasting methods: poll projections, expert... |
Dec 4, 2013
Graefe, Andreas, 2013, "Replication data for: Accuracy of combined forecasts for the 2012 Presidential Election: The PollyVote", https://doi.org/10.7910/DVN/POLLYVOTE2012, Harvard Dataverse, V1
We review the performance of the PollyVote, which combined forecasts from polls, prediction markets, expertsâ judgment, political economy models, and index models to forecast the two-party popular vote in the 2012 U.S. Presidential Election. Throughout the election year the PollyVote provided highly accurate forecasts, outperforming each of its c... |
Nov 21, 2013
Graefe, Andreas, 2013, "Replication data for: Improving forecasts using equally weighted predictors", https://doi.org/10.7910/DVN/EQUALWEIGHTS, Harvard Dataverse, V1
The usual procedure for developing linear models to predict any kind of target variable is to identify a subset of most important predictors and to estimate weights that provide the best possible solution for a given sample. The resulting "optimally" weighted linear composite is then used when predicting new data. This approach is useful in situati... |
