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Forecasting the 2012 Presidential Election with State-Level Economic Indicators

Published online by Cambridge University Press:  27 September 2012

Michael J. Berry  and
Kenneth N. Bickers
Michael J. Berry
Affiliation:
University of Colorado, Denver
Kenneth N. Bickers
Affiliation:
University of Colorado, Boulder
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Extract

Nearly all forecast models of US presidential elections provide estimates of the national two-party vote (Campbell 2008). Each of the nine forecasts published in the 2008 forecasting issue of PS: Political Science and Politics made national popular vote total predictions for the major party candidates, while only one provided an expected result in the Electoral College (Klarner 2008). These national vote models are assumed to be reliable forecasts of who is likely to win the general election. In most cases, this assumption is reasonable. It becomes problematic, however, at precisely the point that forecasts are most interesting: when elections are close. In tight elections, national forecasts can and have produced a “winner” different from the actual winner. Consider the forecasts and ultimate outcome of the 2000 election. Each of the 2000 presidential election forecasts predicted vice president Al Gore to win a majority of the two-party popular vote, which he did, but none correctly predicted governor George W. Bush to assume the presidency (Campbell 2001). Never in US history have White House residents been determined through a national popular vote. Presidential elections are decided through contests in the states and the District of Columbia. The forecast model we developed explicitly models the presidential contest based on factors inherent to these 51 jurisdictions. This modeling approach allows us to make a projection of the Electoral College result, which popular vote estimates cannot.

Type
Symposium: Forecasting the 2012 American National Elections
Information
PS: Political Science & Politics , Volume 45 , Issue 4 , October 2012 , pp. 669 - 674
Copyright
Copyright © American Political Science Association 2012 

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