Controlling for Observed and Unobserved Site Characteristics in RUM Models of Recreation Demand
针对休闲需求模型中场地属性信息不足导致估计偏误的问题，提出一种贝叶斯方法，通过引入完整替代特定常数来隔离旅行成本参数受未观测因素的影响，并在混合Logit框架下实现估计，应用于爱荷华湖泊项目数据。
Recreation demand models are typically plagued by limited information on site attributes. If these unobserved site attributes are correlated with the observed characteristics and/or the travel cost variable, the resulting parameter estimates are likely to be biased. We develop a Bayesian approach to estimating a model that incorporates a full set of alternative‐specific constants, insulating the key travel cost parameter from the influence of unobservables. The proposed posterior simulator can be used in the mixed logit framework in which some parameters of the conditional utility function are random. We apply the estimation procedures to data from the Iowa Lakes Project.