This paper develops a method that improves researchers’ ability to account for behavioral responses to policy change in microsimulation models. Current microsimulation models are relatively simple, in part because of the technical difficulty of accounting for unobserved heterogeneity. This is all the more problematic because data constraints typically force researchers to limit their forecasting models to relatively few, mostly time-invariant explanatory covariates, so that much of the variation across individuals is unobserved. Furthermore, failure to account for unobservables often leads to biased estimates of structural parameters, which are critically important for measuring behavioral responses. This paper develops a theoretical approach to incorporate (univariate and multivariate) unobserved heterogeneity into microsimulation models; illustrates computer algorithms to efficiently implement heterogeneity in continuous and limited dependent models; and evaluates the importance of unobserved heterogeneity by conducting Monte Carlo simulations. Authors’ Acknowledgements This paper benefitted greatly from discussions with Douglas Wolf and Lee Lillard. I also thank Arnstein Aassve, Bonnie Ghosh-Dastidar, Brian Williams, Susan Paddock, Bruce Western, and Jan Saarela for insightful discussions and comments and Neeraj Sood for outstanding research assistance. This research was supported by the Social Security Administration through a grant to the Michigan Retirement Research Center. Correspondence to Constantijn (Stan) Panis, RAND, 1700 Main Street, Santa Monica, CA 90401; e-mail to <email@example.com>.