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

James LeSage (), Manfred Fischer and Thomas Scherngell ()

Papers in Regional Science, 2007, vol. 86, issue 3, 393-421

Abstract: Abstract. We apply a Bayesian hierarchical Poisson spatial interaction model 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. Abstract. Hemos aplicado un modelo de interacción espacial de Poisson Bayesiano jerárquico a la siguiendo la pista a documentación que menciona otras patentes entre patentes de alta tecnología en Europa para identificar y medir efectos de separación espacial de flujos de conocimiento interregionales. El modelo aquí introducido es novedoso en cuanto a que admite efectos de origen y destino espacialmente estructurados para las regiones. La estimación del modelo se lleva a cabo bajo un marco Bayesiano usando métodos de aumento de datos y Monte Carlo basado en cadenas de Markov (MCMC), relacionado con investigación reciente de Frühwirth‐Schnatter y Wagner (2004). Esto permite el muestreo MCMC de familias de distribución bien conocidas, proporcionando así una mejora substancial sobre estimaciones MCMC basadas en muestreo Metropolis‐Hastings de distribuciones condicionales no estándar.

Date: 2007
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https://doi.org/10.1111/j.1435-5957.2007.00125.x

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