Transferable Utility Matching Beyond Logit: Computation and Estimation with General Heterogeneity
超越 logit 的可转移效用匹配:一般异质性下的计算与估计
We present a general framework for matching with transferable utility (TU) that accommodates arbitrary heterogeneity without relying on the logit structure. The optimal assignment problem is characterized by tractable linear programming formulation, allowing flexible error distributions and correlation patterns. We introduce an iterative algorithm that solves large-scale assignment problems with guaranteed convergence and an intuitive economic interpretation, and we show how…