Semiparametric identification in panel data discrete response models
研究了线性指数离散响应面板数据模型中固定效应的半参数识别问题，发现点识别失败但可导出回归系数的信息边界，并通过数值分析展示解释变量支撑集变化对识别边界的影响。
This paper studies semiparametric identification in linear index discrete response panel data models with fixed effects. Departing from the classic binary response static panel data model, this paper examines identification in the binary response dynamic panel data model and the ordered response static panel data model. It is shown that under mild distributional assumptions on the fixed effect and the time-varying unobservables point-identification fails, but informative bounds on the regression coefficients can still be derived. Partial identification is achieved by eliminating the fixed effect and discovering features of the distribution of the unobservable time-varying components that do not depend on the unobserved heterogeneity. Numerical analyses illustrate how the identification bounds change as the support of the explanatory variables varies.