Estimating Spatial Preferences from Votes and Text

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Authors In Song Kim, John Londregan, Marc T. Ratkovic
Journal/Conference Name POLITICAL ANALYSIS
Paper Category
Paper Abstract We introduce a model that extends the standard vote choice model to encompass text. In our model, votes and speech are generated from a common set of underlying preference parameters. We estimate the parameters with a sparse Gaussian copula factor model that estimates the number of latent dimensions, is robust tooutliers, andaccounts for zero inflation in thedata. To illustrate itsworkings,weapply our estimator to roll call votes and floor speech from recent sessions of the US Senate. We uncover two stable dimensions: one ideological and the other reflecting to Senators’ leadership roles. We then show how the method can leverage common speech in order to impute missing data, recovering reliable preference estimates for rank-and-file Senators given only leadership votes.
Date of publication 2018
Code Programming Language R

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