Agenda Constrained Legislator Ideal Points and the Spatial Voting Model
Bibliographic Data
| ID | 7971080 |
|---|---|
| Authors | Joshua D Clinton (0000-0003-1683-9479, Stanford University), Adam Meirowitz (0000-0003-4671-8889, Princeton University) |
| Year | 2001 |
| Volume | 9 |
| Issue | 3 |
| Pages | 242-259 |
| Publication date | 2001-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Political Analysis (JOURNAL) |
| Journal identifiers | ISSN: 1047-1987 • E-ISSN: 1476-4989 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1093/polana/9.3.242 |
| OpenAlex | W2328055171 |
| Language | EN |
| Citations received | 36 |
| References cited | 14 |
Existing preference estimation procedures do not incorporate the full structure of the spatial model of voting, as they fail to use the sequential nature of the agenda. In the maximum likelihood framework, the consequences of this omission may be far-reaching. First, information useful for the identification of the model is neglected. Specifically, information that identifies the proposal locations is ignored. Second, the dimensionality of the policy space may be incorrectly estimated. Third, preference and proposal location estimates are incorrect and difficult to interpret in terms of the spatial model. We also show that the Bayesian simulation approach to ideal point estimation (Clinton et al. 2000; Jackman 2000) may be improved through the use of information about the legislative agenda. This point is illustrated by comparing several preference estimators of the first U.S. House (1789–1791
Bayesian probability · Curse of dimensionality · Econometrics · Economics · Estimation · Estimator · Ideal (ethics · Ideal point · Identification (biology · Legislator · Point (geometry · Political science · Politics · Preference · Spatial analysis · Statistics · Voting · Computer Science · Economic and Environmental Valuation · Housing Market and Economics · Mathematics · Spatial and Panel Data Analysis · Artificial Intelligence · Law
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Multidimensional Analysis of Roll Call Data via Bayesian Simulation
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| Unique citing works | 36 |
|---|---|
| Citations per year | 1,44 |
| Citation span | 2001 - 2025 (25) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 36 |