Spatio-temporal modeling of legislation and votes
Journal Article (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