This paper studies a linear model for multidimensional panel data of three or more dimensions with unobserved interactive fixed-effects. The main estimator uses double debias methods, 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. This is combined with a double debias procedure for asymptotically normal estimates. 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及以上多维面板数据的线性模型。主要估计量采用双重去偏方法，并需要两个预备步骤。首先，将模型嵌入二维面板框架中，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，该变换能够达到参数化的一致收敛速率。结合双重去偏程序，可获得渐近正态的估计量。本文通过啤酒需求弹性的估计案例实现了所提出的方法。