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Forecasting with Unobserved Heterogeneity

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  • Matteo G. Richiardi

Abstract

Forecasting based on random intercepts models requires imputation of the individual permanent effects to the simulated individuals. When these individuals enter the simulation 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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Bibliographic Info

Paper provided by LABORatorio R. Revelli, Centre for Employment Studies in its series LABORatorio R. Revelli Working Papers Series with number 123.

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Date of creation: 2012
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Handle: RePEc:cca:wplabo:123

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Keywords: forecasting; microsimulation; random intercept models; unobserved heterogeneity;

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  1. Constantijn W.A. Panis, 2003. "Microsimulations in the Presence of Heterogeneity," Working Papers wp048, University of Michigan, Michigan Retirement Research Center.
  2. Li, Jinjing & O'Donoghue, Cathal, 2012. "A methodological survey of dynamic microsimulation models," MERIT Working Papers 002, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
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Cited by:
  1. Ambra Poggi & Matteo G. Richiardi, 2012. "Imputing Individual Effects in Dynamic Microsimulation Models.An application of the Rank Method," LABORatorio R. Revelli Working Papers Series 124, LABORatorio R. Revelli, Centre for Employment Studies.

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