Political forecasting

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Political forecasting aims at predicting the outcome of elections. Models include:

Opinion polls[edit]

Polls are a basic part of political forecasting.[1] However, including poll results into political forecasting models can cause problems. This is seen when predicting the outcome of elections. There are a few ways to stay away from inaccurate election forecasts.

Averaging polls[edit]

Combining poll data lowers the forecasting mistakes of a poll.[2] Political forecasting models include averaged poll results, such as the RealClearPolitics poll average.

Poll damping[edit]

Poll Damping is when incorrect marks of public opinion is not used in a forecast model. Early in the campaign, polls are bad measures of the future choices of voters. The poll results closer to an election are a more accurate prediction. Campbell (1996)[3] shows the power of poll damping in political forecasting.

Markets[edit]

Prediction Markets show very accurate forecasts of an election outcome. An example of this is the Iowa Electronic Markets. In a study, 964 election polls were compared with the five US presidential elections from 1988 to 2004. Berg et al. (2008) showed that the Iowa Electronic Markets topped the polls 74% of the time.[4] However, damped polls have been shown to top prediction markets. Comparing damped polls to forecasts of the Iowa Electronic Markets, Erikson and Wlezien (2008) showed that the damped polls outperform all markets or models.

Regression models[edit]

Political scientists and economists oftentimes use regression models of past elections. This is done to help forecast the vote of the two political parties- Democrats and Republicans. The information helps with their parties next presidential candidate forecast for the future. Most models include at least one public opinion variable, a trial heat poll, or a presidential approval rating.

See also[edit]

References[edit]

  1. ^ Andreas Graefe and J. Scott Armstrong. "Predicting elections from the most important issue: A test of the take-the-best heuristic". Journal of Behavioral and Decision Making (forthcoming). 
  2. ^ Alfred G. Cuzan, J. Scott Armstrong, and Randall Jones, "Combining Methods to Forecast the 2004 Presidential Election: The PollyVote"
  3. ^ Campbell J. E. (1996), "Polls and Votes: The Trial-Heat Presidential Election Forecasting Model, Certainty, and Political Campaigns," American Politics Quarterly, 24 (4), pp.408-433.
  4. ^ http://www.biz.uiowa.edu/faculty/trietz/papers/long%20run%20accuracy.pdf