This paper studies a linear model for multidimensional panel data of three or more dimensions with unobserved interactive fixed-effects. The main estimator uses a Neyman-orthogonal approach, and requires two preliminary steps. First, the model is embedded within a two-dimensional panel framework where factor model methods in Bai (2009) lead to consistent, but slowly converging, estimates. The second step develops a weighted-within transformation that is robust to multidimensional interactive fixed-effects and achieves the parametric rate of consistency. The estimator is shown to be asymptotically normal. The methods are implemented to estimate the demand elasticity for beer.

翻译：本文研究了一个包含未观测交互固定效应的三维或更高维面板数据的线性模型。主要估计量采用Neyman正交化方法，需要两个预备步骤。首先，将模型嵌入二维面板框架，其中Bai（2009）的因子模型方法可得到一致但收敛缓慢的估计量。第二步构建了针对多维交互固定效应具有稳健性的加权组内变换，该变换能达到参数化的一致收敛速率。该估计量被证明具有渐近正态性。本文通过估计啤酒需求弹性的实例实现了所提方法。