Spatio-temporal modeling of legislation and votes

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Wang, E; Salazar, E; Dunson, D; Carin, L

Published Date

  • March 22, 2013

Published In

Volume / Issue

  • 8 / 1

Start / End Page

  • 233 - 268

Electronic International Standard Serial Number (EISSN)

  • 1931-6690

International Standard Serial Number (ISSN)

  • 1936-0975

Digital Object Identifier (DOI)

  • 10.1214/13-BA810

Citation Source

  • Scopus