Bayesian Population Forecasting: Extending the Lee-Carter Method

Wiśniowski, Arkadiusz; Smith, Peter; Bijak, Jakub; Raymer, James; Forster, Jonathan J.

doi:10.1007/s13524-015-0389-y

Cited by 74 publications

(52 citation statements)

References 52 publications

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“…In (Raftery et al 2012;Bryant and Graham 2013;Wiśniowski et al 2015) and migration (e.g., Bijak 2011;Abel et al 2013). In fertility studies these , ,...,…”

Section: Hurdle Zero-truncated Poisson Model With Bayesian Approachmentioning

confidence: 99%

Childlessness and fertility by couples' educational gender (in)equality in Austria, Bulgaria, and France

Osiewalska¹

2017

DemRes

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“…In (Raftery et al 2012;Bryant and Graham 2013;Wiśniowski et al 2015) and migration (e.g., Bijak 2011;Abel et al 2013). In fertility studies these , ,...,…”

Section: Hurdle Zero-truncated Poisson Model With Bayesian Approachmentioning

confidence: 99%

Childlessness and fertility by couples' educational gender (in)equality in Austria, Bulgaria, and France

Osiewalska¹

2017

DemRes

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“…There are many social, economic, political and environmental drivers which impact migration flows (Massey et al, 1993), yet there is no single, robust migration theory that can be used for forecasting purposes (Arango, 2000). Besides, even if credible explanations of past migration flows existed, their tenets would be difficult to extrapolate into the futurefor that reason, most of the formal forecasting models rely on time series analysis, be it frequentist or Bayesian (Azose & Raftery, 2015;Bijak, 2010;Bijak & Wiśniowski, 2010;Cappellen, Skjerpen, & Tønnessen, 2015;de Beer, 1993de Beer, , 2008Wiśniowski, Smith, Bijak, Raymer, & Forster, 2015). Besides, even if credible explanations of past migration flows existed, their tenets would be difficult to extrapolate into the futurefor that reason, most of the formal forecasting models rely on time series analysis, be it frequentist or Bayesian (Azose & Raftery, 2015;Bijak, 2010;Bijak & Wiśniowski, 2010;Cappellen, Skjerpen, & Tønnessen, 2015;de Beer, 1993de Beer, , 2008Wiśniowski, Smith, Bijak, Raymer, & Forster, 2015).…”

Section: Introductionmentioning

confidence: 99%

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

Bijak

Disney

Findlay

et al. 2019

Journal of Forecasting

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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.

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“…Wiśniowski et al . () used the Lee–Carter method for all three components of demographic change (fertility, mortality and migration), again using Bayesian methods to obtain predictive probability distributions.…”

Section: Mortality Forecastingmentioning

confidence: 99%

Projecting UK Mortality by Using Bayesian Generalized Additive Models

Hilton

Dodd

Forster

et al. 2018

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary Forecasts of mortality provide vital information about future populations, with implications for pension and healthcare policy as well as for decisions made by private companies about life insurance and annuity pricing. The paper presents a Bayesian approach to the forecasting of mortality that jointly estimates a generalized additive model (GAM) for mortality for the majority of the age range and a parametric model for older ages where the data are sparser. The GAM allows smooth components to be estimated for age, cohort and age‐specific improvement rates, together with a non‐smoothed period effect. Forecasts for the UK are produced by using data from the human mortality database spanning the period 1961–2013. A metric that approximates predictive accuracy is used to estimate weights for the ‘stacking’ of forecasts from models with different points of transition between the GAM and parametric elements. Mortality for males and females is estimated separately at first, but a joint model allows the asymptotic limit of mortality at old ages to be shared between sexes and furthermore provides for forecasts accounting for correlations in period innovations.

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Bayesian Population Forecasting: Extending the Lee-Carter Method

Cited by 74 publications

References 52 publications

Childlessness and fertility by couples' educational gender (in)equality in Austria, Bulgaria, and France

Childlessness and fertility by couples' educational gender (in)equality in Austria, Bulgaria, and France

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

Projecting UK Mortality by Using Bayesian Generalized Additive Models

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