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Below results based on the criteria 'GLMM'
Total number of records returned: 2
Poisson-Normal Dynamic Generalized Linear Mixed Models of U.S. House Campaign Contributions
Mebane, Walter R.
U.S. House of Representatives
We develop generalized linear mixed models to analyze itemized contributions to U.S. House campaigns. Our basic model is a system of Poisson processes that have means that are log-linear functions of normally distributed random effects. Our model permits multiple random effects, including serially correlated effects. The mixed model specification involves an integration over the random effects that is analytically intractable. When there is only one, serially independent random effect, the model may be estimated using quadrature to evaluate the integral. With multiple random effects, quadrature is infeasible but the model may be estimated using the Monte Carlo EM (MCEM) algorithm proposed by McCulloch (1997). We illustrate these various estimation methods. The system we analyze includes contributions to Democratic and Republican candidates from different sources, including individuals and PACs. We estimate dynamic effects both within and across contributions series. The cross-series dynamics measure how contributions to one candidate react to contributions to the other. The cross-series dynamics also measure how contributions to a candidate from one source can mobilize contributions from other sources. We use a combination of observed variables and random effects to test the hypothesis of dynamic mobilization against several hypotheses that imply constant differences between candidates and between districts. One such hypothesis is that some candidates received persistently higher contributions from all sources because of PAC endorsements. Another is that some candidates are simply better at raising money than others. We also test how national expectations about presidential election outcomes affect contributions. We apply our model to itemized contributions data for open seat races in the 1984 election.
Cosponsorship in the U.S. Senate: A Multilevel Approach to Detecting Subtle Social Predictors of Legilslative Support
social network analysis
Why do members of Congress choose to cosponsor legislation proposed by their colleagues and what can we learn from their patterns of cosponsorship? To answer these questions properly requires models that respect the relational nature of the relevant data and the resulting interdependence among observations. We show how the inclusion of carefully selected random effects can capture network-type dependence, allowing us to more confidently investigate senators' propensity to support colleagues' proposals. To illustrate, we examine whether certain social factors such as demographic similarities, opportunities for interaction, and institutional roles are associated with varying odds of cosponsorship during the 2003-04 (108th) Senate.