Imputing Individual Effects in Dynamic Microsimulation Models. An application of the Rank Method

Matteo Richiardi and Ambra Poggi

Department of Economics and Statistics Cognetti de Martiis. Working Papers from University of Turin

Abstract: Dynamic microsimulation modeling 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 estimat ed unconditional distributions lead to biased forecasts, which are often worse than those obtained neglect ing unobserved heterogeneity altogether. We then present a first implementation of the Rank method, a new algorithm for attributing the individual effects to the simulation sample which greatly simplifies those already known in the literature. Out - of - sample validation of our model shows that correctly imputing unobserved heterogeneity significantly improves the quality of the forecasts.

Pages: 36 pages
Date: 2012-09
New Economics Papers: this item is included in nep-cmp and nep-for
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Related works:
Working Paper: Imputing Individual Effects in Dynamic Microsimulation Models. An application of the Rank Method (2012)
Working Paper: Imputing Individual Effects in Dynamic Microsimulation Models.An application of the Rank Method (2012)
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