Bayesian Estimation of the Spatial Durbin Error Model with an Application to Voter Turnout in the 2004 Presidential Election

Abstract

The potential for spatial dependence in models of voter turnout, although plausible from a theoretical perspective, has not been adequately addressed in the literature. Using recent advances in Bayesian computation, the authors formulate and estimate the previously unutilized spatial Durbin error model and apply this model to the question of whether spillovers and unobserved spatial dependence in voter turnout matters from an empirical perspective. Formal Bayesian model comparison techniques are employed to compare the normal linear model, the spatially lagged X model (SLX), the spatial Durbin model, and the spatial Durbin error model. The results overwhelmingly support the spatial Durbin error model as the appropriate empirical model.

On Spatial and Platial Dependence: Examining Shrinkage in Spatially-Dependent Multilevel Models

“…Thus, it is likely that spatial multilevel models with lag specifications have even more complex shrinkage behavior. The systematic formal analysis shown here must be extend to pathbreaking studies of Lacombe et al (2014), Dong and Harris (2015), and Lacombe and McIntyre (2016). Ideally, diagnostics should target an even more general spatial model capable of expressing the complex interactions between multilevel and spatial dependence structures.…”

“…Spatial econometricians and statisticians have indeed been working on models to extend the notions of spatial dependence that are available in multilevel models (Corrado and Fingleton, 2012;Lacombe et al, 2014;Dong and Harris, 2015;Dong and Wu, 2016;Lacombe and McIntyre, 2016). This work typically focuses on multilevel extensions or adaptations of simultaneous autoregressive model structures (Whittle, 1954;Cliff and Ord, 1973;Haining, 1978;Anselin, 1988;Hepple, 2003;LeSage and Pace, 2009).…”

Election Administration within the Sphere of Politics: How Bureaucracy Can Facilitate Democracy with Policy Decisions

“…There are interactions within and between voters, so much so that those spatial interactions must be accounted for to model voting participation accurately. In their analysis, Lacombe et al (2012) suggest that a Spatial Durbin Error Model (SDEM) is better for exploring issues of voter turnout. The particularities of the model will be discussed in the Methods section.…”

“…Gimpel & Schuknecht (2003) used a spatial lag model to deal with the inherent spatial dependence in the data. Following Lacombe et al (2012) the data will be analyzed using a Spatial Durbin Error Model (SDEM). The SDEM adds a spatially autocorrelated error term and lagged versions of the independent variables to the equation (Elhorst 2010).…”

“…Economic Model of Sites RemovedAccessibility Factors Affecting Voting BehaviorsConsistent withDowns (1957),Lacombe et al (2012) found that small disturbances in the environment and timing around casting a vote will sway whether a person participates in an election. This study follows closely the work ofGimpel & Schuknecht's (2003) non-market valuation of the cost of voting as expressed by transportation costs to vote.…”