Partial effects in non-linear panel data models with correlated random effects
系统刻画了非线性面板数据模型中各种偏效应的概念，区分了异质性分布是否随协变量变化以及关注特定值还是平均值，并通过面板Probit示例说明不同偏效应可能差异很大。
Nonlinearity and heterogeneity are known to cause difficulties in estimating and interpreting partial effects. This paper provides a systematic characterization of the various partial effects in nonlinear panel data models that might be of interest to empirical researchers. The interpretation of the partial effects depends upon (i) whether the distribution of unobserved heterogeneity is treated as fixed or allowed to vary with covariates, and (ii) whether one is interested in particular covariate values or an average over such values. The characterization covers partial-effects concepts already in the literature but also includes new concepts for partial effects. A simple panel probit design highlights that the different partial effects can be quantitatively very different.