Welfare Estimation Using Aggregate and Individual‐Observation Models: A Comparison Using Monte Carlo Techniques
用蒙特卡洛模拟比较加总模型与个体观测模型在福利估计中的偏差，发现加总模型（尤其是纳入解释变量区内方差的版本）常优于个体观测模型。
Abstract Due to the weak behavioral foundations of aggregate demand models, zonal travel cost models have been largely abandoned in favor of models based on individual observations. However, sample selection difficulties in individual‐observation models often require the use of distribution‐sensitive limited‐dependent variables estimators. In this paper I use Monte‐Carlo simulations to investigate whether the bias from aggregation is worse than possible bias from these narrowly specified estimators. Somewhat surprisingly, the results indicate that zonal models often outperform the individual‐observation models, especially when using an aggregate model that incorporates intrazonal variance of the explanatory variables.