Replication Data for: A Novel Class of Unfolding Models for Binary Preference Data (doi:10.7910/DVN/SVBF5T)

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Replication Data for: A Novel Class of Unfolding Models for Binary Preference Data

doi:10.7910/DVN/SVBF5T

Harvard Dataverse

2024-08-08

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Lei, Rayleigh; Rodriguez, Abel, 2024, "Replication Data for: A Novel Class of Unfolding Models for Binary Preference Data", https://doi.org/10.7910/DVN/SVBF5T, Harvard Dataverse, V1

Replication Data for: A Novel Class of Unfolding Models for Binary Preference Data

doi:10.7910/DVN/SVBF5T

Lei, Rayleigh (University of Washington)

Rodriguez, Abel (University of Washington)

<i>Political Analysis</i>

Harvard Dataverse

Lei, Rayleigh

Lei, Rayleigh

2024-02-04

https://doi.org/10.7910/DVN/SVBF5T

Social Sciences

We develop a new class of spatial voting models for binary preference data that can accommodate both monotonic and non-monotonic response functions, and are more flexible than alternative “unfolding” models previously introduced in the literature. We then use these models to estimate revealed preferences for legislators in the U.S. House of Representatives and justices on the U.S. Supreme Court. The results from these applications indicate that the new models provide superior complexity-adjusted performance to various alternatives and also that the additional flexibility leads to preferences’ estimates that more closely match the perceived ideological positions of legislators and justices. This dataset contains the code needed to replicate the analysis and figures. Indeed, it includes the replicated figures.

Forthcoming, Political Analysis

Forthcoming, Political Analysis

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probit_unfolding_model_replication.tar.gz

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README.pdf

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