1,721,022 research outputs found
Replication Data for: Accuracy gains from conservative forecasting
No full textProblem: Do conservative econometric models that comply with the Golden Rule of Forecasting provide more accurate forecasts?
Methods: To test the effects of forecast accuracy, we applied three evidence-based guidelines to 19 published regression models used for forecasting 154 elections in Australia, Canada, Italy, Japan, Netherlands, Portugal, Spain, Turkey, U.K., and the U.S. The guidelines direct forecasters using causal models to be conservative to account for uncertainty by (I) modifying effect estimates to reflect uncertainty either by damping coefficients towards no effect or equalizing coefficients, (II) combining forecasts from diverse models, and (III) incorporating more knowledge by including more variables with known important effects.
Findings: Modifying the econometric models to make them more conservative reduced forecast errors compared to forecasts from the original models: (I) Damping coefficients by 10% reduced error by 2% on average, although further damping generally harmed accuracy; modifying coefficients by equalizing coefficients consistently reduced errors with average error reductions between 2% and 8% depending on the level of equalizing. Averaging the original regression model forecast with an equal-weights model forecast reduced error by 7%. (II) Combining forecasts from two Australian models and from eight U.S. models reduced error by 14% and 36%, respectively. (III) Using more knowledge by including all six unique variables from the Australian models and all 24 unique variables from the U.S. models in equal-weight “knowledge models” reduced error by 10% and 43%, respectively.
Originality: This paper provides the first test of applying guidelines for conservative forecasting to established election forecasting models.
Usefulness: Election forecasters can substantially improve the accuracy of forecasts from econometric models by following simple guidelines for conservative forecasting. Decision-makers can make better decisions when they are provided with models that are more realistic and forecasts that are more accurate
Data for Automated Journalism: A Meta-Analysis of Readers’ Perceptions of Human-written vs. Automated News
No full textProvides the coding for the paper "Automated Journalism: A Meta-Analysis of Readers’ Perceptions of Human-written vs. Automated News
Replication Data for: Of Issues and Leaders: Forecasting the 2020 U.S. Presidential Election
No full textData for replicating the forecasts of the Issues and Leaders model for the 2020 U.S. presidential election
Replication Data for: Combining forecasts for the 2021 German federal election: The PollyVote
No full textForecasts of the PollyVote and its components over time for the German federal election 2021, May 1st to June 21
Replication Data for: Forecasting proportional representation elections from non-representative expectation surveys
No full textThis 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 sample of more knowledgeable and interested citizens performed similar to experts and quantitative models, and outperformed the less informed citizens. Furthermore, both citizen samples outperformed prediction markets but provided less accurate forecasts than representative polls. The results suggest that non-representative surveys can provide a useful low-cost forecasting method, in particular for small-scale elections, where it may not be feasible or cost-effective to use established methods such as representative polls or prediction markets
Replication Data for: The PollyVote popular vote forecast for the 2020 U.S. Presidential Election
No full textDaily popular vote forecasts from the combined PollyVot
Replication Data for: Issue-handling beats leadership: Issues and Leaders model predicts Clinton will defeat Trump
No full textThe dataset provides the raw data and forecasts for the Issues and Leaders model to predict the U.S. presidential election 2016
Replication Data for: Accuracy of German federal election forecasts, 2013 and 2017
No full textThe present study reviews the accuracy of four methods (polls, prediction markets, expert judgment, and quantitative models) for forecasting the two German federal elections in 2013 and 2017. On average across both elections, polls and prediction markets were most accurate, while experts and quantitative models were least accurate. The accuracy of individual forecasts did not correlate across elections. That is, methods that were most accurate in 2013 did not perform particularly well in 2017. A combined forecast, calculated by averaging forecasts within and across methods, was more accurate than two out of three component forecasts. The results conform to prior research on US presidential elections in showing that combining is effective in generating accurate forecasts and avoiding large errors
Replication Data for: Forecasting proportional representation elections from non-representative expectation surveys
No full textThis 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 sample of more knowledgeable and interested citizens performed similar to experts and quantitative models, and outperformed the less informed citizens. Furthermore, both citizen samples outperformed prediction markets but provided less accurate forecasts than representative polls. The results suggest that non-representative surveys can provide a useful low-cost forecasting method, in particular for small-scale elections, where it may not be feasible or cost-effective to use established methods such as representative polls or prediction markets
Replication Data for: Combining forecasts for the 2022 French presidential election: The PollyVote
No full textDaily forecasts of the PollyVote and its components for the 2022 French presidential electio
- …
