Combining Aggregate Demand and Discrete Choice Data with Application to Deer License Demand in Indiana
针对休闲活动许可证需求估计中数据变化不足的问题，提出一种结合离散选择实验与市场总需求数据的广义矩方法，使模型更准确复制实际需求。
Estimating demand for licenses for recreational activities is complicated because of a lack of meaningful variation across time, space, buyer types, and license attributes, including price. Prior work uses discrete choice experiments (DCEs) to overcome this challenge, but the resulting demand models are unlikely to replicate observed demands in the absence of ad hoc calibration procedures. We use a generalized method of moments–based approach that combines DCE data with observed market share data to estimate a choice model that yields demand functions that much more closely replicate observed data.