The Performance of Nested Logit Models When Welfare Estimation Is the Goal
通过蒙特卡洛实验和分析，考察嵌套Logit模型在两种设定错误（错误嵌套结构、底层过程不符）下对福利估计的偏差，并评估模型选择标准的作用。
Abstract In this paper we examine the performance of nested logit models in the face of two specification errors. The first specification error arises when a nested logit model is appropriate, but the wrong nesting structure is chosen. The second specification error occurs when the underlying stochastic process is not consistent with nested logit. Particular attention is placed upon the impact that these errors can have on welfare predictions. Monte Carlo experiments are used, together with analytical results, to examine the resulting bias to welfare estimates. In addition, we explore the value of alternative model selection criteria in choosing nesting structures.