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

Ambra Poggi and Matteo Richiardi

No 124, LABORatorio R. Revelli Working Papers Series from LABORatorio R. Revelli, Centre for Employment Studies

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 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 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.

Keywords: Dynamic microsimulation; Unobserved heterogeneity; Validation; Rank method; Assignment algorithms; Female labor force participation; Italy (search for similar items in EconPapers)
JEL-codes: C18 C23 C25 C53 J11 J12 J21 (search for similar items in EconPapers)
Date: 2012
New Economics Papers: this item is included in nep-cmp and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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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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Persistent link: https://EconPapers.repec.org/RePEc:cca:wplabo:124

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