Spatio-temporal modeling of legislation and votes

Eric Wang, Esther Salazar, David Dunson, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

A model is presented for analysis of multivariate binary data with spatio-temporal dependencies, and applied to congressional roll call data from the United States House of Representatives and Senate. The model considers each legislator's constituency (location), the congressional session (time) of each vote, and the details (text) of each piece of legislation. The model can predict votes of new legislation from only text, while imposing smooth temporal evolution of legislator latent features, and correlation of legislators with adjacent constituencies. Additionally, the model estimates the number of latent dimensions required to represent the data. A Gibbs sampler is developed for posterior inference. The model is demonstrated as an exploratory tool of legislation and it performs well in quantitative comparisons to a traditional ideal-point model. © 2013 International Society for Bayesian Analysis.
Original languageEnglish (US)
Pages (from-to)233-268
Number of pages36
JournalBayesian Analysis
Volume8
Issue number1
DOIs
StatePublished - Mar 22 2013
Externally publishedYes

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