Abstract: Estimating school completion is crucial for monitoring SDG 4 on education. The recently introduced SDG indicator 4.1.2, defined as the percentage of children aged 3-5 years above the expected completion age of a given level of education that have completed the respective level, differs from enrolment indicators in that it relies primarily on household surveys. This introduces a number of challenges including gaps between survey waves, conflicting estimates, age misreporting, and delayed completion. Our Adjusted Bayesian Completion Rates (ABC) model addresses these challenges to produce the first complete and consistent time series for SDG indicator 4.1.2, by school level and sex, for 157 countries. The ABC model estimates unobserved true completion rates using a latent ARIMA(1,1,0) with drift process. The model adjusts observations for late completion and age misreporting effects, and also accounts for survey level differences in bias and non-sampling variance. Validation exercises indicate that the model appears well-calibrated and offers a meaningful improvement over simpler approaches in predictive performance.