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Assessing time series models for forecasting international migration: Lessons from the United Kingdom

Assessing time series models for forecasting international migration: Lessons from the United Kingdom
Assessing time series models for forecasting international migration: Lessons from the United Kingdom
Migration is one of the most unpredictable demographic processes. The aim of this article is to provide a blueprint for assessing various possible forecasting approaches in order to help safeguard producers and users of official migration statistics against misguided forecasts. To achieve that, we first evaluate the various existing approaches to modelling and forecasting of international migration flows. Subsequently, we present an empirical comparison of ex post performance of various forecasting methods, applied to international migration to and from the United Kingdom. The overarching goal is to assess the uncertainty of forecasts produced by using different forecasting methods, both in terms of their errors (biases) and calibration of uncertainty. The empirical assessment, comparing the results of various forecasting models against past migration estimates, confirms the intuition about weak predictability of migration, but also highlights varying levels of forecast errors for different migration streams. There is no single forecasting approach that would be well suited for different flows. We therefore recommend adopting a tailored approach to forecasts, and applying a risk management framework to their results, taking into account the levels of uncertainty of the individual flows, as well as the differences in their potential societal impact.
0277-6693
Bijak, Jakub
e33bf9d3-fca6-405f-844c-4b2decf93c66
Disney, George
a91cb1fa-e160-48f1-ac9f-26f8a5504138
Findlay, Allan
6f2552dd-27d4-4a2d-845e-842826584b8a
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Wisniowski, Arkadiusz
343c44c6-7efe-4b2e-9bc7-4ebe0419dd56
Bijak, Jakub
e33bf9d3-fca6-405f-844c-4b2decf93c66
Disney, George
a91cb1fa-e160-48f1-ac9f-26f8a5504138
Findlay, Allan
6f2552dd-27d4-4a2d-845e-842826584b8a
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Wisniowski, Arkadiusz
343c44c6-7efe-4b2e-9bc7-4ebe0419dd56

Bijak, Jakub, Disney, George, Findlay, Allan, Forster, Jonathan J., Smith, Peter W.F. and Wisniowski, Arkadiusz (2019) Assessing time series models for forecasting international migration: Lessons from the United Kingdom. Journal of Forecasting. (doi:10.1002/for.2576).

Record type: Article

Abstract

Migration is one of the most unpredictable demographic processes. The aim of this article is to provide a blueprint for assessing various possible forecasting approaches in order to help safeguard producers and users of official migration statistics against misguided forecasts. To achieve that, we first evaluate the various existing approaches to modelling and forecasting of international migration flows. Subsequently, we present an empirical comparison of ex post performance of various forecasting methods, applied to international migration to and from the United Kingdom. The overarching goal is to assess the uncertainty of forecasts produced by using different forecasting methods, both in terms of their errors (biases) and calibration of uncertainty. The empirical assessment, comparing the results of various forecasting models against past migration estimates, confirms the intuition about weak predictability of migration, but also highlights varying levels of forecast errors for different migration streams. There is no single forecasting approach that would be well suited for different flows. We therefore recommend adopting a tailored approach to forecasts, and applying a risk management framework to their results, taking into account the levels of uncertainty of the individual flows, as well as the differences in their potential societal impact.

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Assessing time series models for forecasting international migration - Accepted Manuscript
Restricted to Repository staff only until 6 February 2021.
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More information

Accepted/In Press date: 31 January 2019
e-pub ahead of print date: 6 February 2019

Identifiers

Local EPrints ID: 428217
URI: https://eprints.soton.ac.uk/id/eprint/428217
ISSN: 0277-6693
PURE UUID: 58d11558-565a-4222-822e-b76024e98803
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: 15 Feb 2019 17:30
Last modified: 20 Jul 2019 01:23

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