@article {10.34196/ijm.00099,
article_type = {journal},
title = {Imputing individual effects in dynamic microsimulation models an application to household formation and labour market participation in Italy},
author = {Richiardi, Matteo and Poggi, Ambra},
volume = 7,
number = 2,
year = 2014,
month = {aug},
pub_date = {2014-08-31},
pages = {3-39},
citation = {IJM 2014;7(2):3-39},
doi = {10.34196/ijm.00099},
url = {https://doi.org/10.34196/ijm.00099},
abstract = {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.},
keywords = {dynamic microsimulation, unobserved heterogeneity, validation, Rank method, assignment algorithms, female labour force participation, Italy},
journal = {IJM},
issn = {1747-5864},
publisher = {International Journal of Microsimulation},
}