Election Prediction: Using National Survey to Predict District-Level Legislative Yuan Elections

Abstract

This paper uses pre-election national survey data and a method combining the Bayesian multilevel modeling approach with the population information for post-stratification (i.e., multilevel regression and post-stratification: MRP) to predict Legislative Yuan elections in the 73 single-member districts. Specifically, our method is consisted of three steps: first, we construct a multilevel logistic regression model to estimate the vote choice variables for the Kuomintang (KMT) and Democratic Progressive Party (DPP) candidates, respectively, given demographics and districts of residence. Second, we post-stratify on all the variables in the model by using the joint population distribution of the demographic variables within each district. Third, we then combine the above two steps and estimate the mean of support for the KMT and DPP candidates in the district level. Given that each district only has about 55 samples on average, this study shows that MRP method can be regarded as an effective tool for election prediction, as the average absolute measurement error between the estimates and actual vote shares is just about 5 percentage points. In a comparison with the pre-election district-level predictions issued by the prediction market \"xFuture\", our estimates are almost as good as the results of \"xFuture\".