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Dynamic microsimulation modelling involves two stages: estimation and forecasting. Unobserved heterogeneity is often considered in estimation, but not in forecasting, beyond trivial cases. Non-trivial cases involve individuals that enter the simulation with a history of previous outcomes. We show that the simple solutions of attributing to these individuals a null effect or a random draw from the estimated unconditional distributions lead to biased forecasts, which are often worse than those obtained neglecting unobserved heterogeneity altogether. We then present a first implementation of the Rank method, a new algorithm for assigning individual effects to the simulation sample. Out-of-sample validation of our model shows that use of the Rank method significantly improves the quality of the forecasts.

Imputing Individual Effects in Dynamic Microsimulation Models An application to household formation and labour market participation in Italy