Improving predictive accuracy of exit polls

Exit polls are best known for their use in election forecasting. In recent years, however, some prominent mistaken predictions have been made, undermining public confidence in the accuracy of both exit polls and survey methods. Nonresponse bias has been claimed as being one of the main reasons for inaccurate projections. Traditionally, the issue has been handled through an age–race–sex adjustment at the national and state levels. An alternative solution is suggested and detailed in this paper. A two-step strategy is proposed to reduce nonresponse bias and improve predictions. First, “vote-remembering” (vote recall) is used to correct party proportion estimates at polling locations; second, this is used to estimate party proportions at precinct level through a regression estimator. The method is gauged by forecasting the 2003 and 2007 Corts Valencianes elections using raw data from the exit polls conducted by SigmaDos for Generalitat Valenciana. In light of the results, this procedure considerably improves raw data projections and shows a substantial improvement over industry (SigmaDos) forecasts. It therefore represents an interesting alternative that could easily be adopted for exit polling in any country where precinct-level voting data exist.