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A comparison of official population projections with Bayesian time series forecasts for England and Wales

A comparison of official population projections with Bayesian time series forecasts for England and Wales
A comparison of official population projections with Bayesian time series forecasts for England and Wales
We compare official population projections with Bayesian time series forecasts for England and Wales. The Bayesian approach allows the integration of uncertainty in the data, models and model parameters in a coherent and consistent manner. Bayesian methodology for time-series forecasting is introduced, including autoregressive (AR) and stochastic volatility (SV) models. These models are then fitted to a historical time series of data from 1841 to 2007 and used to predict future population totals to 2033. These results are compared to the most recent projections produced by the Office for National Statistics. Sensitivity analyses are then performed to test the effect of changes in the prior uncertainty for a single parameter. Finally, in-sample forecasts are compared with actual population and previous official projections. The article ends with some conclusions and recommendations for future work.
0307-4463
95-114
Abel, Guy J.
d35b5069-3c52-4d13-a678-1684ae1fce1e
Bijak, Jakub
e33bf9d3-fca6-405f-844c-4b2decf93c66
Raymer, James
ed2973c1-b78d-4166-baf3-4e18f1b24070
Abel, Guy J.
d35b5069-3c52-4d13-a678-1684ae1fce1e
Bijak, Jakub
e33bf9d3-fca6-405f-844c-4b2decf93c66
Raymer, James
ed2973c1-b78d-4166-baf3-4e18f1b24070

Abel, Guy J., Bijak, Jakub and Raymer, James (2010) A comparison of official population projections with Bayesian time series forecasts for England and Wales. Population Trends, 141 (1), Autumn Issue, 95-114. (doi:10.1057/pt.2010.23). (PMID:20927031)

Record type: Article

Abstract

We compare official population projections with Bayesian time series forecasts for England and Wales. The Bayesian approach allows the integration of uncertainty in the data, models and model parameters in a coherent and consistent manner. Bayesian methodology for time-series forecasting is introduced, including autoregressive (AR) and stochastic volatility (SV) models. These models are then fitted to a historical time series of data from 1841 to 2007 and used to predict future population totals to 2033. These results are compared to the most recent projections produced by the Office for National Statistics. Sensitivity analyses are then performed to test the effect of changes in the prior uncertainty for a single parameter. Finally, in-sample forecasts are compared with actual population and previous official projections. The article ends with some conclusions and recommendations for future work.

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More information

Published date: September 2010
Organisations: Social Statistics & Demography, Southampton Statistical Research Inst., Social Statistics

Identifiers

Local EPrints ID: 182735
URI: http://eprints.soton.ac.uk/id/eprint/182735
ISSN: 0307-4463
PURE UUID: fe082945-aff4-4744-a0fe-aaf6e54fbe75
ORCID for Jakub Bijak: ORCID iD orcid.org/0000-0002-2563-5040

Catalogue record

Date deposited: 28 Apr 2011 10:19
Last modified: 15 Mar 2024 03:34

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Contributors

Author: Guy J. Abel
Author: Jakub Bijak ORCID iD
Author: James Raymer

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