Spatial Econometric Modeling of Presidential Voting Outcomes

Title:	Spatial Econometric Modeling of Presidential Voting Outcomes
Author:	Sutter, Ryan C
Description:	We examine the spatial autoregressive relationship between county-level voting outcomes in the 2000 Presidential election and a host of candidate explanatory variables taken from the year 2000 census. These include: measures of past voting behavior, indicators of socioeconomic demographic status of the population, and economic variables that reflect recent economic conditions. Using a recently developed spatial econometric extension of least-squares regression-based Markov Chain Monte Carlo model composition methodology (often labelled MC^3) by LeSage and Parent(2004), we present evidence on which explanatory variables are important in explaining voting outcomes. The LeSage and Parent (2004) methodology deals with cases where the number of possible models based on different combinations of candidate explanatory variables is large enough that calculation of posterior probabilities for all models is difficult or infeasible. In addition, we produce estimates using a spatial autoregressive seemingly unrelated regression methodology developed in LeSage and Pace (2005), that takes into account cross-equation error covariance between the Bush and Gore equations in the model.
Permanent Link:	http://rave.ohiolink.edu/etdc/view?acc_num=toledo1114618256 http://hdl.handle.net/2374.OX/19259
Date:	2005

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