Estimating separable matching models
针对可转移效用匹配模型中的可分离异质性假设，提出两种简单估计方法：一种基于匹配广义熵的最小距离估计，另一种适用于Choo和Siow模型的广义线性模型估计，均无需求解稳定匹配且表现良好。
Summary Most recent empirical applications of matching with transferable utility have imposed a natural restriction: that the joint surplus be separable in the sources of unobserved heterogeneity. We propose here two simple methods to estimate models in this class. The first method is a minimum distance estimator that relies on the generalized entropy of matching. The second applies to the more special but popular Choo and Siow model, which reformulates as a generalized linear model with two‐way fixed effects. Neither method requires solving for the stable matching. Both methods are easy to apply and perform very well.