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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.

Suggested Citation

  • Matteo G. Richiardi, 2012. "Forecasting with Unobserved Heterogeneity," LABORatorio R. Revelli Working Papers Series 123, LABORatorio R. Revelli, Centre for Employment Studies.
  • Handle: RePEc:cca:wplabo:123
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    File URL: http://www.laboratoriorevelli.it/_pdf/wp123.pdf
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    References listed on IDEAS

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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. Matteo Richiardi & Ambra Poggi, 2012. "Imputing Individual Effects in Dynamic Microsimulation Models. An application of the Rank Method," Carlo Alberto Notebooks 267, Collegio Carlo Alberto.
    2. Matteo Richiardi & Ambra Poggi, 2014. "Imputing Individual Effects in Dynamic Microsimulation Models. An application to household formation and labour market participation in Italy," International Journal of Microsimulation, International Microsimulation Association, vol. 7(2), pages 3-39.

    More about this item

    Keywords

    forecasting; microsimulation; random intercept models; unobserved heterogeneity;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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