International migration data in Europe are collected by individual countries with separate collection systems and designs. As a result, reported data are inconsistent in availability, definition and quality. In this paper, we propose a Bayesian

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The aim of the paper is to present Bayesian forecasts of immigration for seven European countries to 2025, based on quantitative data and qualitative knowledge elicited from country-specific migration experts in a two-round Delphi survey. In line with earlier results, most of the immigration processes under study were found to be barely predictable in the long run, exhibiting non-stationary features. This outcome was obtained largely irrespectively of the expert knowledge input, which nevertheless was found useful in describing the predictive uncertainty, especially in the short term. It is argued that, under the non-stationarity of migration processes, too long forecasts horizons are inadequate, which is a serious challenge for population forecasts in general. Copyright (c) 2010 Royal Statistical Society.

Population and labour force projections are made for 27 selected European countries for 2002-2052, focussing on the impact of international migration on population and labour force dynamics. Starting from single scenarios for fertility, mortality and economic activity, three sets of assumptions are explored regarding migration flows, taking into account probable policy developments in Europe following the enlargement of the EU. In addition to age structures, various support ratio indicators are analysed. The results indicate that plausible immigration cannot offset the negative effects of population and labour force ageing.Keywords Population projections AE Labour force projections AE International migration AE Population ageing AE Europe Ré sumé Des projections de population et de population active sont pré senté es pour 27 pays Europé ens pour la pé riode 2002-2052, avec un inté rê t particulier pour l'impact de la migration internationale sur la dynamique des populations. A partir de scé narios uniques pour la fé condité , la mortalité et l'activité é conomique, trois sé ries d'hypothè ses concernant les flux migratoires sont exploré es, en inté grant des pré -visions sur les dé veloppements des politiques publiques à la suite de l'é largissement de l'Union Europé enne. Les structures par â ge sont analysé es, de mê me que des indicateurs de rapports de dé pendance. Les ré sultats indiquent que les flux d'immigration vraisemblables ne pourront pas compenser les effets né gatifs du vieillissement de la population et de celui de la population active.

In this article, we develop a fully integrated and dynamic Bayesian approach to forecast populations by age and sex. The approach embeds the Lee-Carter type models for forecasting the age patterns, with associated measures of uncertainty, of fertility, mortality, immigration, and emigration within a cohort projection model. The methodology may be adapted to handle different data types and sources of information. To illustrate, we analyze time series data for the United Kingdom and forecast the components of population change to the year 2024. We also compare the results obtained from different forecast models for age-specific fertility, mortality, and migration. In doing so, we demonstrate the flexibility and advantages of adopting the Bayesian approach for population forecasting and highlight areas where this work could be extended.

In this article, we provide a critique of previous estimates of war-related deaths from Bosnia and Herzegovina and propose an analytical framework and a new estimate of such deaths. Our assessment is concentrated on civilian victims, whose death (or disappearance) can in a straightforward manner be linked with war operation. The estimate is based on carefully selected sources analysed jointly at the level of individual records, allowing for identity verification of victims, elimination of duplicates within the sources and exclusion of records overlapping between the sources. Although we can argue that our estimate is much better founded than any other estimate ever obtained, it is still incomplete and should be seen as work in progress. Tabeau, E. et J. Bijak, 2005, De´ce`s dus a`la guerre durant le conflit arme´en BosnieHerze´govine de 1992 a`1995 : critique des estimations existantes et nouveaux re´sultats. Revue Europe´enne de De´mographie, 21: 187-215.Re´sume´. Cet article pre´sente une analyse critique des estimations existantes sur les de´ce`s dus al a guerre en Bosnie-He´rze´govine et propose un cadre d'analyse et une nouvelle estimation de ces de´ce`s. Cette estimation porte sur les victimes civiles, dont la mort (ou la disparition) peut eˆtre relie´e d'une fac¸on directe aux ope´rations de guerre. Elle repose sur une se´lection rigoureuse de sources, traite´es conjointement au niveau des donne´es individuelles, ce qui permet la ve´rification de l'identite´des victimes, l'e´limination des doublons au sein de chaque source et l'identification des cas pre´sents dans plusieurs sources. Meˆme si cette estimation est sans aucun doute meilleure que toutes celles de´ja`publie´es, elle reste incomple`te et doit eˆtre conside´re´e comme une e´tape provisoire dans un travail encore en cours.

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.