Assessing time series models for forecasting international migration : lessons from the United Kingdom
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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.
Bijak , J , Disney , G , Findlay , A M , Forster , J J , Smith , P W F & Wiśniowski , A 2019 , ' Assessing time series models for forecasting international migration : lessons from the United Kingdom ' , Journal of Forecasting , vol. 38 , no. 5 , pp. 470-487 . https://doi.org/10.1002/for.2576
Journal of Forecasting
Copyright © 2019 The Authors Journal of Forecasting Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
DescriptionFunding: This work was funded by the Migration Advisory Committee (MAC), UK Home Office, under the Home Office Science contract HOS/14/040, and also supported by the ESRC Centre for Population Change grant ES/K007394/1.
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