A Novel Class of Unfolding Models for Binary Preference Data
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929652068515840 |
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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 |