Abstract: Structural models of military retention are useful for evaluating policy alternatives that are outside historical experience. However, estimating the parameter coefficients for these models might require solving stochastic, dynamic programs thousands of times for each individual in the data. Even relatively simple models can require significant compute time, which rises rapidly with each additional explanatory variable. Although advances both in computing power and in software over the past few decades have helped increase the size of models that are feasible to estimate, a sufficiently large number of state variables can still render dynamic programming impractical. In this report, the author documents an alternative approach to estimating the coefficients of dynamic discrete choice retention models that avoids the need to solve dynamic programming problems — and, thus, the substantial compute time required to do so.