Replication data for: Agenda Constrained Legislator Ideal Points and the Spatial Voting Model

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).

现有偏好估计流程未能涵盖投票空间模型的完整结构，因其未利用议程的序列性特质。在最大似然（maximum likelihood）框架下，此类疏漏所引发的后果可能影响深远：其一，用于模型识别的有效信息遭到忽视，具体而言，用以确定提案位置的相关信息未被纳入使用；其二，政策空间的维度或许会被错误估算；其三，偏好与提案位置的估计结果存在偏差，且难以依据空间模型进行合理解释。此外，我们证实，借助立法议程的相关信息，可优化用于理想点估计的贝叶斯模拟方法（Clinton等人，2000年；Jackman，2000年）。通过对比美国第一届众议院（1789–1791年）的多款偏好估计器，可对这一改进点进行直观阐释。