International migration flows are considered the most difficult demographic component to forecast and, for that reason, models for forecasting migration are few and relatively undeveloped. This is worrying because, in developed societies, international migration is often the most influential component of population growth and in debates about societal and economic change. The need for better migration forecasts can be addressed, firstly, by combining data from various sources such as official statistics from various countries, surveys and social media data. Secondly, Bayesian inferential framework allows providing estimates of model parameters and forecasts with measures of uncertainty. In this presentation, a selection of Bayesian hierarchical models for combining data from various sources and quantifying uncertainty in migration forecasts will be described.

Arkadiusz (Arek) Wiśniowski is a lecturer in Social Statistics and Demography at the University of Manchester.  Previously, he worked as a Research Fellow at the ESRC Centre for Population Change and Southampton Statistical Sciences Research Institute, University of Southampton. He was a Visiting Fellow at the Australian National University, Researcher at the Central European Forum for Migration and Population Research in Warsaw, and Teaching and Research Assistant at the Department of Applied Econometrics, Warsaw School of Economics.