Knowledge spillovers across Europe: Evidence from a Poisson spatial interaction model with spatial effects

Publication: Scientific journalJournal articlepeer-review


We apply a Bayesian hierarchical Poisson spatial interaction to the paper trail left by patent citations between high-technology patents in Europe to identify and measure spatial separation effects of interregional knowledge flows. The model introduced here is novel in that it allows for spatially structured origin and destination effects for the regions. Estimation of the model is carried out within a Bayesian framework using data augmentation and Markov Chain Monte Carlo (MCMC) methods, related to recent work in Frühwirth-Schnatter and Wagner (2004). This allows MCMC sampling from well-known distribution families, and thus provides a substantial improvement over MCMC estimation based on Metropolis-Hastings sampling from non-standard conditional distributions.
Original languageEnglish
Pages (from-to)393 - 421
JournalPapers in Regional Science. The Journal of the Regional Science Association International
Issue number3
Publication statusPublished - 2007

Cite this