Multidimensional Interactive Fixed-Effects

This paper studies a linear and additively separable model for multidimensional panel data of three or more dimensions with unobserved interactive fixed effects. Two approaches are considered to account for these unobserved interactive fixed-effects when estimating coefficients on the observed covariates. First, the model is embedded within the standard two-dimensional panel framework and restrictions are derived under which the factor structure methods in Bai (2009) lead to consistent estimation of model parameters, but at potentially slow rates of convergence. The second approach utilises popular machine learning techniques to develop group fixed-effects and kernel weighted fixed-effects that are more robust to the multidimensional nature of the problem and can achieve the parametric rate of consistency under certain conditions. Theoretical results and simulations show the benefit of standard two-dimensional panel methods when the structure of the interactive fixed-effect term is known, but also highlight how the group fixed-effects and kernel methods perform well without knowledge of this structure. The methods are implemented to estimate the demand elasticity for beer under a handful of models for demand.