Download Forecasting International Migration in Europe: A Bayesian by Jakub Bijak (auth.) PDF

By Jakub Bijak (auth.)

International migration is turning into an more and more vital component of modern demographic dynamics and but, because of its excessive volatility, it is still the main unpredictable portion of inhabitants switch. In Europe, inhabitants forecasting is principally tricky simply because good-quality facts on migration are missing. there's a transparent want for trustworthy equipment of predicting migration for the reason that inhabitants forecasts are necessary for rational choice making in lots of components, together with labour markets, social safeguard or spatial making plans and business enterprise. In addressing those concerns, this booklet adopts a Bayesian statistical point of view, which permits for a proper incorporation of specialist judgement, whereas describing uncertainty in a coherent and specific demeanour. No past wisdom of Bayesian data is thought. the results are mentioned from the viewpoint of forecast clients (decision makers), with the purpose to teach the relevance and usability of the provided equipment in useful functions. “This is a brilliant publication that represents a step-change within the forecasting of overseas migration. Jakub Bijak advocates for using Bayesian data - a typical option to mix subjective past info with statistical facts. The Bayesian framework presents additionally a traditional approach to extra boost the migration forecasting method that's eventually geared toward accounting for and lowering the various uncertainties, and that comprises cognitive brokers with diversified services - migration specialists, inhabitants forecasters and forecast clients - with a purpose to do so target. The e-book is a needs to for everybody figuring out how migration, specially foreign, will evolve and reply to altering stipulations, occasions and policies.” <Prof. Frans Willekens, Director of the Netherlands Interdisciplinary Demographic Institute (NIDI), The Hague, and Professor of inhabitants stories, college of Groningen

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Extra resources for Forecasting International Migration in Europe: A Bayesian View

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11), the frequency-based definition pertains only to the aleatory concept of uncertainty as the one related to intrinsic randomness. Nevertheless, since the beginning of the twentieth century there have been attempts to create a theory of probability that on one hand would be independent from the frequency of events, and on the other would constitute a logical relation between knowledge (evidence) and rational beliefs. An example of such an approach can be found in Keynes (1921/1973), who interpreted probability as a logical relationship between two sets of alternative propositions.

Different interpretations of probability gave grounds for the parallel development of various philosophies of mathematical statistics. Apart from the traditional inference, based on the notion of frequency of events and repeatable samples, there are also other statistical methodologies that deal with uncertainty in different manners. Thus, the subjectivist approach has been incorporated into Bayesian statistics, which is more thoroughly discussed in the next section. The ideas of Keynes (1921/1973), further developed by Jeffreys (1939/1961, 1946), gave grounds to the notion of ‘objective Bayesianism’, focusing on statistical inference in the instance 8 Popper (1982/1996, p.

3–4). However, this approach also incorporates some problematic issues that remain still unresolved. Firstly, Cohen (1986) noted that uncertainty characterises not only the point forecasts of the variables under study, but also their own uncertainty measures, like the confidence intervals. Secondly, as observed by Clark (2003), demographic models do not account for variability among individuals, which can be another significant source of uncertainty, often overlooked in population forecasting. The latter is a general remark that holds for all macro models without micro-level foundations, not limited to demographic predictions.

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