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Autori principali: Lei, Rayleigh, Rodriguez, Abel
Natura: Preprint
Pubblicazione: 2023
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Accesso online:https://arxiv.org/abs/2308.16288
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author Lei, Rayleigh
Rodriguez, Abel
author_facet Lei, Rayleigh
Rodriguez, Abel
contents 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 are closer matches to the perceived ideological positions of legislators and justices.
format Preprint
id arxiv_https___arxiv_org_abs_2308_16288
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle A Novel Class of Unfolding Models for Binary Preference Data
Lei, Rayleigh
Rodriguez, Abel
Applications
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 are closer matches to the perceived ideological positions of legislators and justices.
title A Novel Class of Unfolding Models for Binary Preference Data
topic Applications
url https://arxiv.org/abs/2308.16288