Feasible Estimation of Linear Models with N-Fixed Effects

Abstract

In this paper an alternative approach for the estimation of higher-order linear fixed-effects models is described. The strategy relies on the transformation of the data prior to calculating estimations of the model. While the approach is computationally intensive, the hardware requirements for the estimation process are minimal, allowing for the estimation of models with more than two high-order fixed effects for large datasets. An illustration of the implementation is presented using the US Census Bureau Current Population Survey data with four fixed effects.