Template-type: ReDIF-Paper 1.0
Author-Name: Richiardi Matteo
Author-Email: matteo.richiardi@unito.it
Author-Name: Poggi Ambra 
Author-Email: ambra.poggi@unimib.it
Author-Workplace-Name: University of Turin
Author-Workplace-Homepage: http://www.est.unito.it/
Title: Imputing Individual Effects in Dynamic Microsimulation Models. An application of the Rank Method
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. 
Length: 36 pages
Creation-Date: 2012-09
File-URL: http://www.est.unito.it/do/home.pl/Download?doc=/allegati/wp2012dip/13_wp_richiardi_poggi.pdf
File-Format: Application/PDF
Handle: RePEc:uto:dipeco:201213