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We present a fast and memory efficient algorithm for the estimation ofgeneralized linear models with an additive separable k-way error component. Thebrute force approach uses dummy variables to account for the unobservedheterogeneity, but quickly faces computational limits. Thus, we show how aweighted version of the Frisch-Waugh-Lovell theorem combined with the method ofalternating projections can be incorporated into a Newton-Raphson algorithm todramatically reduce the computational costs. The algorithm is especially usefulin situations, where generalized linear models with k-way fixed effects basedon dummy variables are computationally demanding or even infeasible due to timeor memory limitations. In a simulation study and an empirical application wedemonstrate the performance of our algorithm.

Fast and Feasible Estimation of Generalized Linear Models with High-Dimensional k-way Fixed Effects