Forecasting with Unobserved Heterogeneity

Forecasting based on random intercepts models requires imputation of the individual permanent effects to the simulated individuals. When these individuals enter the simula_x000d_ tion with a history of past outcomes this involves sampling from conditional distributions, which might be unfeasible. I present a method for drawing individual permanent effects from a conditional distribution which only requires to invert the corresponding estimated unconditional distribution. While the algorithms currently available in the literature require polynomial time, the proposed method only requires matching two ranks and works therefore in N lnN time.

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