Identification of Average Marginal Effects in Fixed Effects Dynamic Discrete Choice Models
针对短面板非线性模型中固定效应方法无法识别平均边际效应的批评，本文证明在T最小为3的动态logit模型中，不同平均边际效应（包括滞后因变量变化或持续时间的因果效应）可被点识别，并给出基于选择历史概率的简单闭式表达式。
Abstract In nonlinear panel data models, fixed-effects methods are often criticized because they cannot identify average marginal effects (AMEs) in short panels. In contrast with that criticism, we prove the point identification of different AMEs, including causal effects of changes in the lagged dependent variable or the last choice's duration, in a panel dynamic logit model for T as small as three. Our proofs are constructive and provide simple closed-form expressions for the AMEs in terms of probabilities of choice histories. We illustrate our results using Monte Carlo experiments and with an empirical application of a dynamic model of consumer brand choice.