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Integrating uncertainty in time series population forecasts: An illustration using a simple projection model

Integrating uncertainty in time series population forecasts: An illustration using a simple projection model
Integrating uncertainty in time series population forecasts: An illustration using a simple projection model
Background: Population forecasts are widely used for public policy purposes. Methods to quantify the uncertainty in forecasts tend to ignore model uncertainty and to be based on a single model.

Objective: In this paper, we use Bayesian time series models to obtain future population estimates with associated measures of uncertainty. The models are compared based on Bayesian posterior model probabilities, which are then used to provide model-averaged forecasts.

Methods: The focus is on a simple projection model with the historical data representing population change in England and Wales from 1841 to 2007. Bayesian forecasts to the year 2032 are obtained based on a range of models, including autoregression models, stochastic volatility models and random variance shift models. The computational steps to fit each of these models using the OpenBUGS software via R are illustrated.

Results: We show that the Bayesian approach is adept in capturing multiple sources of uncertainty in population projections, including model uncertainty. The inclusion of non-constant variance improves the fit of the models and provides more realistic predictive uncertainty levels. The forecasting methodology is assessed through fitting the models to various truncated data series.
1187-1226
Abel, Guy J.
4625e2a7-a0ed-4430-8fa2-82fc3ae276c3
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
Wong, Jackie S.T.
bc6647ec-62bc-4b36-b883-2afe7c529f65
Abel, Guy J.
4625e2a7-a0ed-4430-8fa2-82fc3ae276c3
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
Wong, Jackie S.T.
bc6647ec-62bc-4b36-b883-2afe7c529f65

Abel, Guy J., Bijak, Jakub, Forster, Jonathan J., Raymer, James, Smith, Peter W.F. and Wong, Jackie S.T. (2013) Integrating uncertainty in time series population forecasts: An illustration using a simple projection model. Demographic Research, 29 (43), 1187-1226. (doi:10.4054/DemRes.2013.29.43).

Record type: Article

Abstract

Background: Population forecasts are widely used for public policy purposes. Methods to quantify the uncertainty in forecasts tend to ignore model uncertainty and to be based on a single model.

Objective: In this paper, we use Bayesian time series models to obtain future population estimates with associated measures of uncertainty. The models are compared based on Bayesian posterior model probabilities, which are then used to provide model-averaged forecasts.

Methods: The focus is on a simple projection model with the historical data representing population change in England and Wales from 1841 to 2007. Bayesian forecasts to the year 2032 are obtained based on a range of models, including autoregression models, stochastic volatility models and random variance shift models. The computational steps to fit each of these models using the OpenBUGS software via R are illustrated.

Results: We show that the Bayesian approach is adept in capturing multiple sources of uncertainty in population projections, including model uncertainty. The inclusion of non-constant variance improves the fit of the models and provides more realistic predictive uncertainty levels. The forecasting methodology is assessed through fitting the models to various truncated data series.

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Published date: 10 December 2013

Identifiers

Local EPrints ID: 418862
URI: https://eprints.soton.ac.uk/id/eprint/418862
PURE UUID: 131baf14-d396-4de3-a41d-99468fb32e83
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

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Date deposited: 23 Mar 2018 17:30
Last modified: 24 May 2019 00:38

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Contributors

Author: Guy J. Abel
Author: Jakub Bijak ORCID iD
Author: James Raymer
Author: Jackie S.T. Wong

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