TY - JOUR
T1 - Accounting for spatial error correlation in the 2004 presidential popular vote
AU - Lacombe, Donald J.
AU - Shaughnessy, Timothy M.
PY - 2007/7
Y1 - 2007/7
N2 - One problem with describing election vote shares using ordinary least squares (OLS) is that it ignores the possible presence of spatial error correlation, whereby the errors are correlated in a systematic manner over space. This omission can bias OLS standard errors. We examine the 2004 presidential county vote outcome using OLS and a spatial error model (SEM) that accounts for spatial autocorrelation in the error structure. We find that spatial error correlation is present, that the SEM is superior to OLS for making inferences, and that several factors deemed important to the 2004 election outcome are not significant once the spatial error autocorrelation is taken into account.
AB - One problem with describing election vote shares using ordinary least squares (OLS) is that it ignores the possible presence of spatial error correlation, whereby the errors are correlated in a systematic manner over space. This omission can bias OLS standard errors. We examine the 2004 presidential county vote outcome using OLS and a spatial error model (SEM) that accounts for spatial autocorrelation in the error structure. We find that spatial error correlation is present, that the SEM is superior to OLS for making inferences, and that several factors deemed important to the 2004 election outcome are not significant once the spatial error autocorrelation is taken into account.
KW - Presidential election
KW - Spatial econometrics
KW - Spatial error model
UR - http://www.scopus.com/inward/record.url?scp=34249999244&partnerID=8YFLogxK
U2 - 10.1177/1091142106295768
DO - 10.1177/1091142106295768
M3 - Article
AN - SCOPUS:34249999244
SN - 1091-1421
VL - 35
SP - 480
EP - 499
JO - Public Finance Review
JF - Public Finance Review
IS - 4
ER -