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Title: Replication data for: Ideal Point Estimation with a Small Number of Votes: A Random-Effects Approach      
dateReleased:
02-16-2010
downloadURL: http://hdl.handle.net/1902.1/14261
ID:
hdl:1902.1/14261
description:
Many conventional ideal point estimation techniques are inappropriate when only a limited number of votes are available. This paper presents a covariate-based random-effects Bayesian approach that allows scholars to estimate ideal points based on fewer votes than required for fixed-effects models. Using covariates brings more information to bear on the estimation; using a Bayesian random-effects approach avoids incidental parameter problems. Among other things, the method allows us to estimate directly the effect of covariates such as party on preferences and to estimate standard errors for ideal points. Monte Carlo results, an empirical application, and a discussion of further applications demonstrate the usefulness of the method.
description:
Michael Bailey, 2010, "Replication data for: Ideal Point Estimation with a Small Number of Votes: A Random-Effects Approach", http://hdl.handle.net/1902.1/14261, Harvard Dataverse, V1
name:
Michael Bailey
homePage: http://www.harvard.edu/
name:
Harvard University
ID:
SCR:011273
abbreviation:
DataVerse
homePage: http://thedata.org/
name:
Dataverse Network Project
ID:
SCR:001997