Stochastic Population Forecasts Based on Conditional Expert Opinions

Billari, Francesco C.; Graziani, Rebecca; Melilli, Eugenio

doi:10.1111/j.1467-985x.2011.01015.x

Cited by 27 publications

(33 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Expert evaluations are elicited according to the conditional procedure suggested by Billari et al (2012). For all indicators, experts are asked to provide both marginal and conditional, central and high, scenarios.…”

Section: The Proposed Approachmentioning

confidence: 99%

“…We build on the conditional expert-based approach of Billari et al (2012). Our contribution addresses two main issues.…”

Section: Introductionmentioning

confidence: 99%

“…For each time t of the forecasting interval [t 0 , T], the forecast is obtained through interpolation between the starting known and the final random values. In Billari et al (2012), the probability distribution of each indicator over the interval [t 0 , T] is specified using expert opinions at time T on the indicator, conditional on its value at an intermediate point. More precisely, after having fixed a point t 1 in (t 0 , T), the expert is required to elicit the mean and a specific upper quantile for each indicator at time t 1 ; in a second step, the mean and a specific upper quantile of the same indicator at time T, given its value at time t 1 .…”

Section: Introductionmentioning

confidence: 99%

“…To this aim, we designed a questionnaire in collaboration with researchers of the Italian National Statistical Institute (ISTAT), an official forecasting agency; we then submitted the questionnaire online to a panel of Italian demographers. The questionnaire is an enhanced version of the elicitation procedure introduced by Billari et al (2012).…”

Section: Introductionmentioning

confidence: 99%

“…As in 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: that is, fertility, mortality, and migration. Here, two main purposes are pursued.…”

mentioning

confidence: 99%

See 4 more Smart Citations

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

2014

View full text Add to dashboard Cite

This article suggests a procedure to derive stochastic population forecasts adopting an expert-based approach. As in 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: 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 SupraBayesian 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.

show abstract

Section: The Proposed Approachmentioning

confidence: 99%

“…We build on the conditional expert-based approach of Billari et al (2012). Our contribution addresses two main issues.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

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

2014

View full text Add to dashboard Cite

show abstract

Elicitation

Wolfson¹,

Bousquet²

2016

Wiley StatsRef: Statistics Reference Online

View full text Add to dashboard Cite

show abstract

Stochastic Population Forecasting: A Bayesian Approach Based on Evaluation by Experts

Graziani

2020

Developments in Demographic Forecasting

View full text Add to dashboard Cite

We suggest a procedure for deriving expert based stochastic population forecasts within the Bayesian approach. According to the traditional and commonly used cohort-component model, the inputs of the forecasting procedures are the fertility and mortality age schedules along with the distribution of migrants by age. Age schedules and distributions are derived from summary indicators, such as total fertility rates, male and female life expectancy at birth, and male and female number of immigrants and emigrants. The joint distributions of all summary indicators are obtained based on evaluations by experts, elicited according to a conditional procedure that makes it possible to derive information on the centres of the indicators, their variability, their across-time correlations, and the correlations between the indicators. The forecasting method is based on a mixture model within the Supra-Bayesian approach that treats the evaluations by experts as data and the summary indicators as parameters. The derived posterior distributions are used as forecast distributions of the summary indicators of interest. A Markov Chain Monte Carlo algorithm is designed to approximate such posterior distributions.

show abstract

Stochastic Population Forecasts Based on Conditional Expert Opinions

Cited by 27 publications

References 37 publications

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

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

Elicitation

Stochastic Population Forecasting: A Bayesian Approach Based on Evaluation by Experts

Contact Info

Product

Resources

About