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Imputing Individual Effects in Dynamic Microsimulation Models. An application of the Rank Method

Author

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  • Matteo Richiardi
  • Ambra Poggi
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 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 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.

Suggested Citation

  • 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.
    • Handle: RePEc:cca:wpaper:267
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    Cited by:

    1. Matteo Richiardi & Ross E. Richardson, 2017. "JAS-mine: A new platform for microsimulation and agent-based modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 106-134.
    2. Jessica M. Mc Lay & Roy Lay-Yee & Barry J. Milne & Peter Davis, 2015. "Regression-Style Models for Parameter Estimation in Dynamic Microsimulation: An Empirical Performance Assessment," International Journal of Microsimulation, International Microsimulation Association, vol. 8(2), pages 83-127.
    3. 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.
    4. Ross Richardson & Lia Pacelli & Ambra Poggi & Matteo Richiardi, 2018. "Female Labour Force Projections Using Microsimulation for Six EU Countries APPENDIX," International Journal of Microsimulation, International Microsimulation Association, vol. 11(2), pages 52-83.

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    More about this item

    Keywords

    Dynamic microsimulation; Unobserved heterogeneity; Validation; Rank method; Assignment algorithms; Female labor force participation; Italy;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • J12 - Labor and Demographic Economics - - Demographic Economics - - - Marriage; Marital Dissolution; Family Structure
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure

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