Linear Probability Models of the Demand for Attributes with an Empirical Application to Estimating the Preferences of Legislators
构建了一个严格论证的线性概率模型，用于分析二元选择（如投票）中未观测属性的需求，并应用于估计美国国会议员的投票偏好。研究发现，投票空间的有效维度高于传统研究估计，表明议员投票不仅受意识形态影响，还受议题特定属性的重要影响。
This paper formulates and estimates a rigorously-justified linear probability model of binary choices over alternatives characterized by unobserved attributes. The model is applied to estimate preferences of congressmen as expressed in their votes on bills. The effective dimension of the attribute space characterizing votes is larger than what has been estimated in recent influential studies of voting by Poole and Rosenthal. Congressmen vote on more than ideology. Issue-specific attributes are an important determinant of congressional voting patterns. The estimated dimension is too large for the median voter model to describe voting