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In this paper, the authors develop a new estimation method that is suitable for censored models with two high-dimensional fixed effects and that is based on a sequence of least squares regressions, yielding significant savings in computing time and hence making it applicable to frameworks in which standard estimation techniques become unfeasible. The authors analyze its theoretical properties and evaluate its practical performance in small samples through a detailed Monte Carlo study. Finally, using a longitudinal match employer-employee dataset from Spain, they show that the biases encountered when ignoring censored issues can be significant to the role of firms in terms of wage dispersion: individual heterogeneity explains more than 60% of wage dispersion.