Forecasting Principles:
Damping polls
Evidence from the literature shows that polls, in particular early in the campaign, are not reliable in predicting election outcomes but tend to overestimate the extent to which a candidate leads. To deal with these uncertainties, we added a damping factor to the RCP poll average. Damping is used to make forecasts more conservative in situations involving high uncertainty.
Our poll damping is based on research conducted by Campbell (1996) who showed that polls have to be discounted in order to achieve more reliable forecasts. Performing a regression analysis on historical poll data for the elections from 1948 to 2004, he derived a formula for discounting the polls according to their distance from Election Day. Campbell provided Polly the formula, along with a list of damping factors that vary by the number of days left before the election.
Currently, polls are discounted with a damping factor of ${factor}. Applying this factor, we calculate Polly’-s discounted poll based forecast thus:
Polly’-s poll based forecast = ((Latest RCP polling average – 50) * (1 – [damping factor])) + 50 = ((46.1 – 50) * (1 – 0.17)) + 50 = 46.8
Latest RCP polling average 46.1
Damping factor 0.17
Polly’-s poll based forecast 46.8
Thus, our poll damping discounts a candidate’-s lead in the two-party vote, depending on the days left prior to election. The further away the election day, the larger the damping.
Such damped polls have been shown to outperform sophisticated forecasting approaches like prediction markets. Comparing damped polls to forecasts of the Iowa Electronic Markets, Erikson and Wlezien (2008) showed that the damped polls outperformed both the winner-take-all and the vote-share markets.
Thanks to Andreas Graefe for the link.
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Are Political Markets Really Superior to Polls as Election Predictors? – PDF file
For now, our results suggest the need for much more caution and less naive cheerleading about election markets on the part of prediction market advocates.
Previously: The truth about prediction markets
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