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Comparison of two election prediction models: an empirical analysis of Taiwan election survey data

In this paper, we compare our Voter's Request Indicator model, developed in 1998 from the theory of marketing, with the Logistic Prediction Model which has been used more widely. Both models perform well in terms of accuracy and stability of prediction outcome. However, concerning speed and ease-of-use, the computational process of the Logistic Prediction Model is much more complicated than that of our Voter's Request Indicator Election Prediction model. In addition, the Voter's Request Indicator Election Prediction model takes both potential and already-committed voters into consideration, and uses a ratio of factors to calculate the tendency intensity, which is indicative of internal and external circumstances influencing candidate support. In contrast, the Logistic Prediction Model cannot indicate the tendency intensity of candidates, so policy makers could be confused and make wrong decisions which could affect the election outcome.