Spatial and sectoral linkages in regional models: A Bayesian vector autoregression forecast evaluation*

Dan Rickman, Steven Miller and Russell McKenzie

Papers in Regional Science, 2009, vol. 88, issue 1, 29-41

Abstract: Abstract A Bayesian vector autoregression (BVAR) approach is used to assess whether prior information on spatial and economic base‐sectoral linkages improves forecast accuracy of employment for the metropolitan areas of the state of Oklahoma and their proximate metropolitan areas. Compared to autoregressive and vector autoregressive alternatives, a BVAR with a gravity‐based prior is found to improve forecast accuracy of aggregate metropolitan employment. In bifurcated employment models, a prior of proportionality between basic and nonbasic sectors outperformed a prior of an inverse relationship between the two sectors implied by a fixed‐factor‐full‐employment general equilibrium model. Resumen Se utiliza un planteamiento de autorregresión vectorial Bayesiana (BVAR) para evaluar si la información a priori sobre vínculos espaciales y sectoriales del tejido económico mejora la precisión en la predicción del empleo en las áreas metropolitanas del estado de Oklahoma y sus áreas metropolitanas más inmediatas. Comparado con alternativas de autorregresión y autorregresión vectorial, se ha descubierto que un BVAR a priorístico de gravedad mejora la precisión en la predicción de empleo metropolitano agregado. En modelos de empleo bifurcados, una proporcionalidad a priori entre sectores básicos y no básicos tuvo mucho mejor comportamiento que una relación inversa a priori entre los dos sectores implicados con un modelo de equilibrio general de factor‐fijo‐empleo‐total.

Date: 2009
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https://doi.org/10.1111/j.1435-5957.2008.00170.x

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Persistent link: https://EconPapers.repec.org/RePEc:bla:presci:v:88:y:2009:i:1:p:29-41

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