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Stochastic population forecasts based on conditional expert opinions

Author

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  • Francesco C. Billari
  • Rebecca Graziani
  • Eugenio Melilli

Abstract

We develop a method for the derivation of expert-based stochastic population forecasts. The full probability distribution of forecasts is specified by expert opinions on future developments, elicited conditional on the realization of high, central, low scenarios. The procedure is applied to forecast the Italian population, using scenarios from the Italian National Statistical Office (ISTAT) and the Statistical Office of the European Union (EUROSTAT).

Suggested Citation

  • Francesco C. Billari & Rebecca Graziani & Eugenio Melilli, 2010. "Stochastic population forecasts based on conditional expert opinions," Working Papers 033, "Carlo F. Dondena" Centre for Research on Social Dynamics (DONDENA), Università Commerciale Luigi Bocconi.
  • Handle: RePEc:don:donwpa:033
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    File URL: ftp://ftp.dondena.unibocconi.it/WorkingPapers/Dondena_WP033.pdf
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    Cited by:

    1. Gianni Corsetti & Marco Marsili, 2013. "Previsioni stocastiche della popolazione nell’ottica di un Istituto Nazionale di Statistica," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 15(2-3), pages 5-29.
    2. repec:bdi:workqs:qse_08 is not listed on IDEAS
    3. Atsede D. Tegegne & Marianne Penker & Maria Wurzinger, 2016. "Participatory Demographic Scenarios Addressing Uncertainty and Transformative Change in Ethiopia," Systemic Practice and Action Research, Springer, vol. 29(3), pages 277-296, June.
    4. Matteo Gomellini & Cormac O' Grada, 2011. "Outward and Inward Migrations in Italy: A Historical Perspective," Quaderni di storia economica (Economic History Working Papers) 08, Bank of Italy, Economic Research and International Relations Area.
    5. Francesco Billari & Rebecca Graziani & Eugenio Melilli, 2014. "Stochastic Population Forecasting Based on Combinations of Expert Evaluations Within the Bayesian Paradigm," Demography, Springer;Population Association of America (PAA), vol. 51(5), pages 1933-1954, October.
    6. Robert Stewart & Marie Urban & Samantha Duchscherer & Jason Kaufman & April Morton & Gautam Thakur & Jesse Piburn & Jessica Moehl, 2016. "A Bayesian machine learning model for estimating building occupancy from open source data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1929-1956, April.
    7. Heinz Stefan, 2014. "Uncertainty quantification of world population growth: A self-similar PDF model," Monte Carlo Methods and Applications, De Gruyter, vol. 20(4), pages 261-277, December.

    More about this item

    Keywords

    stochastic population forecasting; random scenario; conditional expert opinions; Italian population forecasts;

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