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Citizen Forecasting: Can Voters See into the Future?

Published online by Cambridge University Press:  27 January 2009

Extract

Political science, unlike economics, does not have a long tradition of forecasting models. However, this is changing. Currently, there is considerable interest in election forecasting. The basis for the interest is a flurry of related publications on House, Senate and presidential elections. A common goal for these studies is the development of a model, inevitably based on aggregate time-series data, which predicts election returns. The resulting models, some of which are quite accurate, can differ a good deal in specification and estimation. Also, they vary in complexity, making them more or less accessible to the engaged voter.

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Notes and Comments
Copyright
Copyright © Cambridge University Press 1989

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References

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3 Another possible variable to predict forecast accuracy would be whether a voter had been following the polls. Unfortunately, this question was never asked in these NES data (with the exception of a 1980 panel). However, we do have plentiful data on media usage, which would be the vehicle for poll-watching. The media variable (see Table 1) is significant in only two of the eight contests. Thus, while information about polls is available almost exclusively through the media, the tendency to be a heavy media consumer makes little difference in our model. Furthermore, and perhaps more important, there is evidence that, although poll stories have become a much more common news event over time (e.g., the number of poll stories on American network television news broadcasts more than doubled between 1972 and 1980), the overall ability of the electorate to predict the winner of presidential elections follows no such time trend (see Figure 1).

4 Lewis-Beck, , ‘Election Forecasts in 1984: How Accurate Were They?’Google Scholar

5 The results given in Equation 1, although based on a rather small sample, are quite robust; that is, they are not dependent upon one or two influential observations. Deleting each observation in turn, then re-estimating, never lowers the goodness-of-fit statistic (R 2) more than a few percentage points.

6 Kramer, Gerald H., ‘The Ecological Fallacy Revisited: Aggregate- versus Individual-Level Findings on Economics and Elections and Sociotropic Voting’, American Political Science Review, 77 (1983), 92111.CrossRefGoogle Scholar