The paper suggests a procedure to derive stochastic population forecasts adopting an expert-based approach. As in a previous work by Billari et al. (2012), experts are required to provide evaluations, in the form of conditional and unconditional scenarios, on summary indicators of the demographic components determining the population evolution, i.e. fertility, mortality and migration. Here two main purposes are pursued. First, the demographic components are allowed to have some kind of dependence. Second, as a result of the existence of a body of shared information, possible correlations among experts are taken into account. In both cases, the dependence structure is not imposed by the researcher but it is indirectly derived through the scenarios elicited from the experts. To address these issues, the method is based on a mixture model, within the so-called Supra-Bayesian approach according to which expert evaluations are treated as data. The derived posterior distribution for the demographic indicators of interest is used as forecasting distribution and a Markov Chain Monte Carlo algorithm is designed to approximate this posterior. The paper provides the questionnaire which was designed by the authors to collect expert opinions. Finally, an application to the forecast of the Italian Population from 2010 up to 2065 is proposed.

Stochastic Population Forecasting Based on Combinations of Expert Evaluations within the Bayesian Paradigm

BILLARI, FRANCESCO CANDELORO;GRAZIANI, REBECCA;MELILLI, EUGENIO
2014

Abstract

The paper suggests a procedure to derive stochastic population forecasts adopting an expert-based approach. As in a previous work by Billari et al. (2012), experts are required to provide evaluations, in the form of conditional and unconditional scenarios, on summary indicators of the demographic components determining the population evolution, i.e. fertility, mortality and migration. Here two main purposes are pursued. First, the demographic components are allowed to have some kind of dependence. Second, as a result of the existence of a body of shared information, possible correlations among experts are taken into account. In both cases, the dependence structure is not imposed by the researcher but it is indirectly derived through the scenarios elicited from the experts. To address these issues, the method is based on a mixture model, within the so-called Supra-Bayesian approach according to which expert evaluations are treated as data. The derived posterior distribution for the demographic indicators of interest is used as forecasting distribution and a Markov Chain Monte Carlo algorithm is designed to approximate this posterior. The paper provides the questionnaire which was designed by the authors to collect expert opinions. Finally, an application to the forecast of the Italian Population from 2010 up to 2065 is proposed.
2014
Billari, FRANCESCO CANDELORO; Graziani, Rebecca; Melilli, Eugenio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/3881119
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