3 datasets found
  1. H

    Replication data for: Forecasts of the 2012 U.S. presidential election based...

    • dataverse.harvard.edu
    Updated Nov 4, 2014
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    Andreas Graefe; J. Scott Armstrong (2014). Replication data for: Forecasts of the 2012 U.S. presidential election based on candidates’ perceived competence in handling the most important issue [Dataset]. http://doi.org/10.7910/DVN/22949
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 4, 2014
    Dataset provided by
    Harvard Dataverse
    Authors
    Andreas Graefe; J. Scott Armstrong
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    United States
    Description

    The Big-Issue Model predicts election outcomes based on voters’ perceptions of candidates’ ability to handle the most important issue. The model provided accurate forecasts of the 2012 U.S. presidential election. The results demonstrate the usefulness of the model in situations where one issue clearly dominates the campaign, such as the state of the economy in the 2012 election. In addition, the model is particularly valuable if economic fundamentals disagree, a situation in which forecasts from traditional political economy models suggest high uncertainty. The model provides immediate feedback to political candidates and parties on the success of their campaign and can advise them on which issues to assign the highest priority.

  2. d

    March Madness Predictions

    • datahub.io
    Updated Sep 25, 2024
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    (2024). March Madness Predictions [Dataset]. https://datahub.io/core/five-thirty-eight-datasets/datasets/march-madness-predictions
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    Dataset updated
    Sep 25, 2024
    Description

    This folder contains data behind the 2014 NCAA Tournament Predictions.

    This dataset was scraped from FiveThirtyEight - march-madness-predictions ...

  3. H

    Replication data for: Accuracy of combined forecasts for the 2012...

    • dataverse.harvard.edu
    Updated Dec 5, 2013
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    Andreas Graefe (2013). Replication data for: Accuracy of combined forecasts for the 2012 Presidential Election: The PollyVote [Dataset]. http://doi.org/10.7910/DVN/POLLYVOTE2012
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 5, 2013
    Dataset provided by
    Harvard Dataverse
    Authors
    Andreas Graefe
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    We review the performance of the PollyVote, which combined forecasts from polls, prediction markets, experts’ 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 component methods, as well as the forecasts from FiveThirtyEight.com. Gains in accuracy were particularly large early in the campaign, when uncertainty about the election outcome is typically high. The results confirm prior research showing that combining is one of the most effective approaches to generating accurate forecasts.

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Share
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TwitterTwitter
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Click to copy link
Link copied
Close
Cite
Andreas Graefe; J. Scott Armstrong (2014). Replication data for: Forecasts of the 2012 U.S. presidential election based on candidates’ perceived competence in handling the most important issue [Dataset]. http://doi.org/10.7910/DVN/22949

Replication data for: Forecasts of the 2012 U.S. presidential election based on candidates’ perceived competence in handling the most important issue

Related Article
Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Nov 4, 2014
Dataset provided by
Harvard Dataverse
Authors
Andreas Graefe; J. Scott Armstrong
License

CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically

Area covered
United States
Description

The Big-Issue Model predicts election outcomes based on voters’ perceptions of candidates’ ability to handle the most important issue. The model provided accurate forecasts of the 2012 U.S. presidential election. The results demonstrate the usefulness of the model in situations where one issue clearly dominates the campaign, such as the state of the economy in the 2012 election. In addition, the model is particularly valuable if economic fundamentals disagree, a situation in which forecasts from traditional political economy models suggest high uncertainty. The model provides immediate feedback to political candidates and parties on the success of their campaign and can advise them on which issues to assign the highest priority.

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