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

Replication data and code forthcoming 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. (2003), 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 1st US House (1789-1791)

复制数据与代码即将发布。现有偏好估计方法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征。在最大似然框架下，此类建模遗漏的影响可能极为深远：其一，用于模型识别的有效信息被忽视，具体而言，可用于确定提案位置的信息未被纳入考量；其二，政策空间的维度可能被错误估计；其三，偏好与提案位置的估计结果存在偏差，且难以结合投票空间模型进行合理解读。本研究还表明，针对理想点估计的贝叶斯模拟方法（Clinton等人(2003)、Jackman(2000)）可通过引入立法议程相关信息得到优化。通过对比美国第一届众议院（1789-1791年）的多款偏好估计方法，可对上述观点进行阐释。