Functional Differencing
针对非线性面板数据中的附带参数问题，提出一种系统方法，通过正交投影消除个体固定效应，构建不受个体效应影响的公共参数矩条件，并证明估计量的一致性和渐近正态性。
In nonlinear panel data models, the incidental parameter problem remains a challenge to econometricians. Available solutions are often based on ingenious, model-specific methods. In this paper, we propose a systematic approach to construct moment restrictions on common parameters that are free from the individual fixed effects. This is done by an orthogonal projection that differences out the unknown distribution function of individual effects. Our method applies generally in likelihood models with continuous dependent variables where a condition of non-surjectivity holds. The resulting method-of-moments estimators are root-N consistent (for fixed T) and asymptotically normal, under regularity conditions that we spell out. Several examples and a small-scale simulation exercise complete the paper.