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Although the initial tests appear to be
promising, there are a variety of possible
explanations for the results. In order to tease
out a more definitive causal statement from the
data, it is necessary to use a multivariate
model to control for extraneous explanations.
Using
election data from 1984 through 2000, it is
possible to construct a cross-sectional time
series
dataset.
There are several options available for
cross-sectional, time-series data analysis. Box-
Jenkins ARIMA or Maximum Likelihood Estimation
procedures are commonly used methods,
but they often force the analyst to examine
multiple series of potentially dissimilar cases
(Zuk
and Thompson, 1982). Since the data are pooled
cross-sections, Feasible Generalized Least
Squares (FGLS) pooled regression is a more
appropriate choice1. FGLS can take into account
AR(1) autocorrelations (unlike many other tests)
while still producing unbiased estimators and
compensating for heteroscedaticity within the
panel data.
In order to control for other possible
explanations, we include variables that parallel
most
of the common explanations of individual-level
voter turnout: age, education, rural/urban, and
income. We included two additional variables:
the percentage of the county that is American
the election returns are from the archives of
the Inter-university
Consortium for Political and Social Research,
and the casino information was drawn from data
at
the Bureau of Indian Affairs.
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