Stochastic Population Forecasting Based on Combinations of Expert Evaluations Within the Bayesian Paradigm.
This article suggests a procedure to derive stochastic population forecasts adopting an expert-based approach. As in previous work by Billari et al. (), experts are required to provide evaluations, in the form of conditional and unconditional scenarios, on summary indicators of the demographic compo...
| Publicado en: | Demography (Springer Nature) Vol. 51; no. 5; pp. 1933 - 1955 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Oct2014
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=99008114&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 99008114 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00703370 DEM jtl: Demography (Springer Nature) issn: 00703370 maglogo: N pubinfo: dt: Oct2014 vid: 51 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 99008114 10.1007/s13524-014-0318-5 ppf: 1933 ppct: 22 formats: fmt: @attributes: type: P size: 473KB tig: atl: Stochastic Population Forecasting Based on Combinations of Expert Evaluations Within the Bayesian Paradigm. aug: au: Billari, Francesco Graziani, Rebecca Melilli, Eugenio affil: Department of Sociology and Nuffield College, University of Oxford, Oxford UK Department of Policy Analysis and Public Management and Carlo F. Dondena Center for Research on Social Dynamics, Bocconi University, Milan Italy Department of Decision Sciences, Bocconi University, Milan Italy su: Population forecasting Stochastic analysis Markov chain Monte Carlo Demographic research Bayesian analysis Expertise Mathematical models sug: subj: Population forecasting Research and Development in the Social Sciences and Humanities Stochastic analysis Markov chain Monte Carlo Demographic research Bayesian analysis Expertise Mathematical models keyword: Conditional elicitation Mixture model Random scenarios Supra-Bayesian Conditional elicitation Mixture model Random scenarios Supra-Bayesian ab: This article suggests a procedure to derive stochastic population forecasts adopting an expert-based approach. As in previous work by Billari et al. (), 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: that is, 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 rather 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. This article provides the questionnaire designed by the authors to collect expert opinions. Finally, an application to the forecast of the Italian population from 2010 to 2065 is proposed. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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