  We present a fast and memory efficient algorithm for the estimation of
generalized linear models with an additive separable k-way error component. The
brute force approach uses dummy variables to account for the unobserved
heterogeneity, but quickly faces computational limits. Thus, we show how a
weighted version of the Frisch-Waugh-Lovell theorem combined with the method of
alternating projections can be incorporated into a Newton-Raphson algorithm to
dramatically reduce the computational costs. The algorithm is especially useful
in situations, where generalized linear models with k-way fixed effects based
on dummy variables are computationally demanding or even infeasible due to time
or memory limitations. In a simulation study and an empirical application we
demonstrate the performance of our algorithm.
