Modeling preference heterogeneity in stated choice data: an analysis for public goods generated by agriculture
比较了随机参数Logit、潜在类别模型和协方差异质性模型在农业公共物品陈述选择数据中的表现，发现潜在类别模型拟合最佳，但所有模型预测能力相当。
Abstract Stated choice models based on the random utility framework are becoming increasingly popular in the applied economics literature. The need to account for respondents’ preference heterogeneity in such models has motivated researchers in agricultural, environmental, health, and transport economics to apply random parameter logit and latent class models. In most of the published literature these models incorporate heterogeneity in preferences through the systematic component of utility. An alternative approach is to investigate heterogeneity through the random component of utility, and covariance heterogeneity models are one means of doing this. In this article we compare these alternative ways of incorporating preference heterogeneity in stated choice models and evaluate the sensitivity of estimated welfare measures to which approach is selected. We find that a latent class approach fits our data best, but all the models perform well in terms of out‐of‐sample predictions. Finally, we discuss what criteria a researcher can use to decide which approach is most appropriate for a given data set.