{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T09:26:13Z","timestamp":1791278773466,"version":"4.1.0"},"reference-count":27,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2008,2,28]],"date-time":"2008-02-28T00:00:00Z","timestamp":1204156800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J of Applied Econometrics"],"published-print":{"date-parts":[[2008,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This study investigates the pattern of knowledge spillovers arising from patent activity between European regions. A Bayesian hierarchical model is developed that specifies region\u2010specific latent effects parameters modeled using a connectivity structure between regions that can reflect geographical proximity in conjunction with technological and other types of proximity. This approach exploits the fact that interregional relationships may exhibit industry\u2010specific technological linkages or transportation network linkages, which is in contrast to traditional studies relying exclusively on geographical proximity. We also allow for both symmetric and asymmetric knowledge spillovers between regions, and for heterogeneity across the regional sample. A series of formal Bayesian model comparisons provides support for a model based on technological proximity combined with spatial proximity, asymmetric knowledge spillovers, and heterogeneity in the disturbances. Estimates of region\u2010specific latent effects parameters structured in this fashion are produced by the model and used to draw inferences regarding the character of knowledge spillovers across the regions. The method is illustrated using sample data on patent activity covering 323 regions in nine European countries. Copyright \u00a9 2008 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/jae.981","type":"journal-article","created":{"date-parts":[[2008,2,28]],"date-time":"2008-02-28T05:46:30Z","timestamp":1204177590000},"page":"235-256","source":"Crossref","is-referenced-by-count":179,"title":["Using the variance structure of the conditional autoregressive spatial specification to model knowledge spillovers"],"prefix":"10.1002","volume":"23","author":[{"given":"Olivier","family":"Parent","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James P.","family":"LeSage","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2008,2,28]]},"reference":[{"key":"e_1_2_9_2_1","first-page":"13","article-title":"Space and growth: a survey of empirical evidence and methods","volume":"21","author":"Abreu M","year":"2005","journal-title":"Region et Developpement"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1006\/juec.1997.2032"},{"key":"e_1_2_9_4_1","first-page":"630","article-title":"R&D spillovers and the geography of innovation and production","volume":"86","author":"Audretsch DB","year":"1996","journal-title":"American Economic Review"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/10438590100000010"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214501753382282"},{"key":"e_1_2_9_7_1","first-page":"1267","article-title":"Discussion: What is a statistical model? 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Using the variance structure of the conditional autoregressive spatial specification to model knowledge spillovers. 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