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What do Bayesian methods offer population forecasters?

What do Bayesian methods offer population forecasters?
What do Bayesian methods offer population forecasters?
The Bayesian approach has a number of attractive properties for probabilistic forecasting. In this paper, we apply Bayesian time series models to obtain future population estimates with uncertainty for England and Wales. To account for heterogeneity found in the historical data, we add parameters to represent the stochastic volatility in the error terms. Uncertainty in model choice is incorporated through Bayesian model averaging techniques. The resulting predictive distributions from Bayesian forecasting models have two main advantages over those obtained using traditional stochastic models. Firstly, data and uncertainties in the parameters and model choice are explicitly included using probability distributions. As a result, more realistic probabilistic population forecasts can be obtained. Second, Bayesian models formally allow the incorporation of expert opinion, including uncertainty, into the forecast. Our results are discussed in relation to classical time series methods and existing cohort component projections. This paper demonstrates the flexibility of the Bayesian approach to simple population forecasting and provides insights into further developments of more complicated population models that include, for example, components of demographic change.
2042-4116
6
1-26
ESRC Centre for Population Change
Abel, Guy J.
d35b5069-3c52-4d13-a678-1684ae1fce1e
Bijak, Jakub
e33bf9d3-fca6-405f-844c-4b2decf93c66
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Raymer, James
ed2973c1-b78d-4166-baf3-4e18f1b24070
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Abel, Guy J.
d35b5069-3c52-4d13-a678-1684ae1fce1e
Bijak, Jakub
e33bf9d3-fca6-405f-844c-4b2decf93c66
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Raymer, James
ed2973c1-b78d-4166-baf3-4e18f1b24070
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940

Abel, Guy J., Bijak, Jakub, Forster, Jonathan J., Raymer, James and Smith, Peter W.F. (2010) What do Bayesian methods offer population forecasters? (ESRC Centre for Population Change Working Paper, 6) UK. ESRC Centre for Population Change

Record type: Monograph (Working Paper)

Abstract

The Bayesian approach has a number of attractive properties for probabilistic forecasting. In this paper, we apply Bayesian time series models to obtain future population estimates with uncertainty for England and Wales. To account for heterogeneity found in the historical data, we add parameters to represent the stochastic volatility in the error terms. Uncertainty in model choice is incorporated through Bayesian model averaging techniques. The resulting predictive distributions from Bayesian forecasting models have two main advantages over those obtained using traditional stochastic models. Firstly, data and uncertainties in the parameters and model choice are explicitly included using probability distributions. As a result, more realistic probabilistic population forecasts can be obtained. Second, Bayesian models formally allow the incorporation of expert opinion, including uncertainty, into the forecast. Our results are discussed in relation to classical time series methods and existing cohort component projections. This paper demonstrates the flexibility of the Bayesian approach to simple population forecasting and provides insights into further developments of more complicated population models that include, for example, components of demographic change.

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More information

Published date: 1 June 2010
Organisations: Social Sciences, Centre for Population Change

Identifiers

Local EPrints ID: 160745
URI: https://eprints.soton.ac.uk/id/eprint/160745
ISSN: 2042-4116
PURE UUID: 62d858ed-2193-4734-ba01-7664e7c51a0e
ORCID for Jakub Bijak: ORCID iD orcid.org/0000-0002-2563-5040
ORCID for Jonathan J. Forster: ORCID iD orcid.org/0000-0002-7867-3411
ORCID for Peter W.F. Smith: ORCID iD orcid.org/0000-0003-4423-5410

Catalogue record

Date deposited: 21 Jul 2010 08:20
Last modified: 24 May 2019 00:38

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