Theories of voting tell us that party vote dynamics have two core components, long-run equilibria and more or less rapid returns to equilibria after short-run deviations. Statistical models used to represent these theories, however, tend to emphasize one component or the other. The unbalanced emphasis leaves the theoretical ideas underspecified and produces biased estimates of both long- and short-run components of vote dynamics. We specify the two components in the familiar form of an error correction model and demonstrate its advantages in terms of its theoretical consistency, simplicity, and precise prediction of whether something is wrong. We illustrate its usefulness by applying it to two sets of analyses reported in the literature and showing that it usually changes the conclusions reported in regard to both the equilibrium levels of party competition and the strength of restoring forces.

The use of the proximity model to represent the relationship between citizens' policy attitudes and the positions of candidates on the issues of the day has considerable appeal because it offers a bridge between theoretical models of political behavior and empirical work. However, there is little consensus among applied researchers about the appropriate representation of voter behavior with respect to the measurement of issue distance, candidate location, or whether to allow heterogeneity in the weight that each individual places on particular issues. Each of these choices suggests a different, and reasonably complicated, nonlinear relationship between voter utility and candidate and voter issue positions which may have a meaningful influence on the substantive conclusions drawn by the researcher. Yet, little attention has been given to the best way to represent the proximity model in applied work. The purpose of this paper is to identify which forms of the proximity model work best, with particular consideration given to the identification of functional forms that are invariant to the choice of scale for the independent variables.

A variety of factors have been shown to influence position timing and the content of positions taken by legislators on important issues. In addition to these observed factors, I argue that unobserved factors such as behind-the-scenes lobbying and party loyalty may also influence position timing and position content. Although hypotheses about observed factors can be tested using traditional methods, hypotheses about unobserved factors cannot. To test for systematic effects of unobserved factors on position timing and content, I develop a seemingly unrelated discrete-choice duration estimator and apply it to data from the vote for the North American Free Trade Agreement. The results indicate that even after controlling for observed factors, there is still evidence that unobserved factors such as Presidential lobbying and/or party loyalty influence both choices.

We provide an introduction to the regression discontinuity design (RDD) and use the technique to evaluate models of sequential Senate elections predicting that the winning party for one Senate seat will receive fewer votes in the next election for the other seat. Using data on U.S. Senate elections from 1946 to 2004, we find strong evidence that the outcomes of the elections for the two Senate seats are independent.

Using large-scale network analysis I map the cosponsorship networks of all 280,000 pieces of legislation proposed in the U.S. House and Senate from 1973 to 2004. In these networks, a directional link can be drawn from each cosponsor of a piece of legislation to its sponsor. I use a number of statistics to describe these networks such as the quantity of legislation sponsored and cosponsored by each legislator, the number of legislators cosponsoring each piece of legislation, the total number of legislators who have cosponsored bills written by a given legislator, and network measures of closeness, betweenness, and eigenvector centrality. I then introduce a new measure I call “connectedness” which uses information about the frequency of cosponsorship and the number of cosponsors on each bill to make inferences about the social distance between legislators. Connectedness predicts which members will pass more amendments on the floor, a measure that is commonly used as a proxy for legislative influence. It also predicts roll call vote choice even after controlling for ideology and partisanship.