A Latent Class Approach to Modeling Endogenous Spatial Sorting in Zonal Recreation Demand Models
提出一种将未观测异质性纳入总量计数数据框架的方法，用于控制分区休闲模型中的内生空间排序，通过野外徒步者数据验证，发现潜在类别模型比同质性假设模型产生的福利估计更小。
A method for incorporating unobserved heterogeneity into aggregate count data frameworks is presented and used to control for endogenous spatial sorting in zonal recreation models. The method is based on latent class analysis, which has become a popular tool for analyzing heterogeneous preferences with individual data but has not yet been applied to aggregate count data. The method is tested using data on backcountry hikers for a southern California study site and performs well for relatively small numbers of classes. The latent class model produces substantially smaller welfare estimates compared to a constrained version that assumes homogeneity throughout the population.