Monte Carlo methods in spatio-temporal regression modeling of migration in the EU

Abstract

Spatio-temporal regression models are well developed in disciplines such as, for example, climate and geostatistics, but have had little application in the modelling of economic phenomena. In this study we have modelled migrations of skilled workers and firms across the European Union during the period 1998-2010. The data set has been extracted from Eurostats Labour Force Survey (LFS) and contains information stratified by European region. We investigate whether the spatial component in the migration patterns is based either on neighbourhood or on some other metric (such as the existence of a flight connection). The complete spatio-temporal model has been implemented using conditional autoregressive (CAR) random effects in the Bayesian framework. In recent years, Bayesian methods have been widely applied to spatio-temporal modelling since they enable the use of Markov chain Monte Carlo (MCMC) samplers to estimate model parameters. In this paper, we consider the Bayesian Adaptive Independence Sampler (BAIS) for estimation, and compare different computing schemes. The results suggest that the regions with a stronger increase of skilled workers are more likely to have similarities with other advanced regions which they are connected to by flight connections, than with the regions at their border. The conclusion of this study is that graphical proximity is not a sufficient condition to reduce differences in skill endowments between regions.