Predicting Elections from Biographical Information about Candidates: A test of the index method
with Scott Armstrong.
Forthcoming in Journal of Business Research.
Click here for a pre-final version of the paper.
Abstract. We used the take-the-best heuristic to develop a model to forecast the popular two- party vote shares in U.S. presidential elections. The model draws upon information about how voters expect the candidates’ to deal with the most important issue facing the country. We used cross-validation to calculate a total of 1,000 out-of-sample forecasts, one for each of the last 100 days of the ten U.S. presidential elections from 1972 to 2008. Ninety-seven percent of forecasts correctly predicted the winner of the popular vote. For the six most recent elections, the model provided more correct predictions of the winner than did the Iowa Electronic Markets (IEM), although the IEM provided more accurate vote share predictions. In predicting the vote shares for the last three elections from 2000 to 2008, the model was as accurate as the typical econometric model. In using information about the frequency of Internet searches, the model allows for easy tracking of issue importance and early identification of emerging issues and, thus, can suggest which issues candidates should stress in their campaign.
An earlier version was presented at the Symposium on Leadership and Individual Differences, Lausanne, November 30 - December 1, 2009.
