Autologistic Models With An Application To Us Presidential Primaries Considering Spatial And Temporal Dependence

The US presidential primaries take place sequentially in different places with a time lag. However, they have not attracted as much attention in terms of modelling as the US presidential election has. This study applied several autologistic models to find the relation between the outcome of the primary election for a Democrat candidate with socioeconomic attributes in consideration of spatial and temporal dependence. According to the result applied to the 2016 election data at the county level, Hillary Clinton was supported by people in counties with high population rates of old age, Black, female and Hispanic. In addition, spatial dependence was observed, representing that people were likely to support the same candidate who was supported from neighboring counties. Positive auto-correlation was also observed in the time-series of the election outcome. Among several autologistic models of this study, the model specifying the effect of Super Tuesday had the best fit.