Ideal Point Estimation
Ideal point estimation recovers the latent policy positions — ideal points — of political actors from their observed binary choices, most often legislators' yea/nay votes on roll calls. Building on the spatial theory of voting and formalized as a Bayesian item-response model by Clinton, Jackman, and Rivers in 2004, it places each legislator and each bill in a low-dimensional policy space and estimates positions so that the probability a legislator votes yea increases as the bill's 'yea' outcome moves closer to that legislator's ideal point.
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来源
- Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370. DOI: 10.1017/S0003055404001194 ↗
- Jackman, S. (2001). Multidimensional Analysis of Roll Call Data via Bayesian Simulation: Identification, Estimation, Inference, and Model Checking. Political Analysis, 9(3), 227–241. DOI: 10.1093/polana/9.3.227 ↗
- Poole, K. T., & Rosenthal, H. (1997). Congress: A Political-Economic History of Roll Call Voting. New York: Oxford University Press. ISBN: 9780195055771
如何引用本页
ScholarGate. (2026, June 22). Ideal Point Estimation (Bayesian Spatial Voting Models). ScholarGate. https://scholargate.app/zh/political-science/ideal-point-estimation
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