There is increased interest in value-added models relying on longitudinal student-level test score data to isolate teachers' contributions to student achievement. The complex linkage of students to teachers as students progress through grades poses both substantive and computational challenges. This article introduces a multivariate Bayesian formulation of the longitudinal model developed by McCaffrey, Lockwood, Koretz, Louis, and Hamilton (2004) that explicitly parameterizes the long-term effects of past teachers on student outcomes in future years and shows how the Bayesian approach makes estimation feasible even for large data sets. The article presents empirical results using reading and mathematics achievement data from a large urban school district, providing estimates of teacher effect persistence and examining how different assumptions about persistence impact estimated teacher effects. It also examines the impacts of alternative methods of accounting for missing teacher links and of joint versus marginal modeling of reading and mathematics. (Contains 4 figures and 1 table.)