00000cmm a22000004i 4500 UP-99796217611036976 Buklod 20230504003249.0 m go j cr cn |||aaaaa 101029s2011 xx u | eng d 978-90-481-8897-0 (eBook) (iLib)UPD-00208004190 DML eng DMLUC JV 7590 B55 2011eb Bijak, Jakub. Forecasting international migration in Europe a Bayesian view [electronic resource] Jakub Bijak, author. Dordrecht Springer Netherlands 2011. 1 online resource (xxiii, 308 p.) ill. Springer series on demographic methods and population analysis 24 PART I. INTRODUCTION; 1. Introduction and background; 2. Preliminaries; PART II. EXPLAINING AND FORECASTING MIGRATION; 3. Explaining migration: brief overview of selected theories; 4. Forecasting migration: selected models and methods; PART III. EXAMPLES OF BAYESIAN MIGRATION PREDICTIONS; 5. Bayesian model selection and forecast averaging; 6. Bayesian VAR modelling from general to specific; 7. Selected approaches to discontinuities in trends; 8. Evaluation of presented forecasts of European migration; 9. Bayesian computing in practice (Contributed by Arkadiusz Wiśniowski); PART IV. PERSPECTIVES OF FORECAST MAKERS AND USERS; 10. Extensions and limitations of migration forecasts; 11. Dealing with uncertain forecasts: A policy perspective; PART V. CONCLUSION; 12. Summary and conclusion: beyond migration forecasting; Acknowledgements; References; Subject Index; Index of Names; ANNEX A. Data sources and the preparatory work; ANNEX B. WinBUGS code used in the presented Bayesian forecasts; ANNEX C. Bayesian forecasts of selected migration flows in Europe. IP based subscription, on campus and remote access. Access via Electronic Resources of the UPD University Library Website. International migration is becoming an increasingly important element of contemporary demographic dynamics and yet, due to its high volatility, it remains the most unpredictable element of population change. In Europe, population forecasting is especially difficult because good-quality data on migration are lacking. There is a clear need for reliable methods of predicting migration since population forecasts are indispensable for rational decision making in many areas, including labour markets, social security or spatial planning and organisation. In addressing these issues, this book adopts a Bayesian statistical perspective, which allows for a formal incorporation of expert judgement, while describing uncertainty in a coherent and explicit manner. No prior knowledge of Bayesian statistics is assumed. The outcomes are discussed from the point of view of forecast users (decision makers), with the aim to show the relevance and usefulness of the presented methods in practical applications. This is a great book that represents a step-change in the forecasting of international migration. Jakub Bijak advocates for the use of Bayesian statistics - a natural way to combine subjective prior information with statistical data. The Bayesian framework provides also a natural way to further develop the migration forecasting process that is ultimately aimed at accounting for and reducing the different uncertainties, and that involves cognitive agents with different expertise - migration experts, population forecasters and forecast users - in order to accomplish that aim. The book is a must for everyone interested knowing how migration, especially international, will evolve and respond to changing conditions, events and policies. <Prof. Frans Willekens, Director of the Netherlands Interdisciplinary Demographic Institute (NIDI), The Hague, and Professor of Population Studies, University of Groningen. Electronic reproduction. New York SpringerLink 2011. Available via World Wide Web through SpringerLink. Bayesian statistical decision theory. European Union countries Emigration and immigration. European Union countries Emigration and immigration Forecasting Statistical methods. European Union countries Emigration and immigration Mathematical models. Electronic books. SpringerLink (Online service). Electronic Resource Available for University of the Philippines Diliman via SpringerLink. Click here to access http://link.springer.com/book/10.1007/978-90-481-8897-0 FO Monograph UPD DMLR Electronic Resource