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Hail to the Fundraiser-in-Chief: The Evolution of Presidential Fundraising Travel, 1977-2004
Unformatted Document Text:  average, presidents now fundraise in more states than they used to, but presidents still travel a great deal to states in which they don't attend fundraising events. Assessing Factors Related to Fundraising and Other Presidential Travel tion and nd nalysis itudinal, with repeated observations of states over time, I use a fi To assess more effectively the relationships between presidential atten electoral factors, we turn our attention to regression analysis. Because the dependent variable, the number of non-fundraiser-related presidential public events held in each state in each year, is a count of an event, which only takes on positive, integer values a is not normally distributed, regression analysis using ordinary least squares would yield inefficient, inconsistent, and biased estimates. Instead I use maximum likelihood techniques to estimate a negative binomial regression model, a type of regression a for over-dispersed 2 count data. 3 Because the data are long xed effects negative binomial model. 4 This model takes into account that there likely is unobserved heterogeneity among the states in the study – that is, that they vary in ways that are not measured by the variables in the model. The fixed effects estimator calculates coefficients by looking at variation within each state but not across states, so unobserved heterogeneity among states does not affect the model’s estimates. Table 6 presents the results of two fixed effects negative binomial models, the first with non- 2 I.e., the variance is greater than the mean. 3 See Cameron, A. Colin and Pravin K. Trivedi. 1998. Regression Analysis of Count Data. Cambridge: Cambridge University Press; and Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, California: Sage Publications. 4 This model was run using STATA’s xtnbreg function, which estimates negative binomial models for longitudinal data. Doherty 15

Authors: Doherty, Brendan.
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average, presidents now fundraise in more states than they used to, but presidents still
travel a great deal to states in which they don't attend fundraising events.
Assessing Factors Related to Fundraising and Other Presidential Travel
tion and
nd
nalysis
itudinal, with repeated observations of states over time, I
use a fi
To assess more effectively the relationships between presidential atten
electoral factors, we turn our attention to regression analysis. Because the dependent
variable, the number of non-fundraiser-related presidential public events held in each
state in each year, is a count of an event, which only takes on positive, integer values a
is not normally distributed, regression analysis using ordinary least squares would yield
inefficient, inconsistent, and biased estimates. Instead I use maximum likelihood
techniques to estimate a negative binomial regression model, a type of regression a
for over-dispersed
count data.
Because the data are long
xed effects negative binomial model.
This model takes into account that there
likely is unobserved heterogeneity among the states in the study – that is, that they vary
in ways that are not measured by the variables in the model. The fixed effects estimator
calculates coefficients by looking at variation within each state but not across states, so
unobserved heterogeneity among states does not affect the model’s estimates. Table 6
presents the results of two fixed effects negative binomial models, the first with non-
2
I.e., the variance is greater than the mean.
3
See Cameron, A. Colin and Pravin K. Trivedi. 1998. Regression Analysis of Count Data. Cambridge:
Cambridge University Press; and Long, J. Scott. 1997. Regression Models for Categorical and Limited
Dependent Variables
. Thousand Oaks, California: Sage Publications.
4
This model was run using STATA’s xtnbreg function, which estimates negative binomial models for
longitudinal data.
Doherty
15


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