We investigate whether commonly used value-added estimation strategies can produce accurate estimates of teacher effects. We estimate teacher effects in simulated student achievement data sets that mimic plausible types of student grouping and teacher assignment scenarios. No one method accurately captures true teacher effects in all scenarios, and the potential for misclassifying teachers as high- or low-performing can be substantial. Misspecifying dynamic relationships can exacerbate estimation problems. However, some estimators are more robust across scenarios and better suited to estimating teacher effects than others.