Assessing Regional Economic Performance: Regional Competition in Spain Under a Spatial Vector Autoregressive Approach

Miguel A. Márquez, Julián Ramajo, Geoffrey J.D. Hewings

Research output: Chapter in Book/Report/Conference proceedingChapter


After a wave of empirical literature on regional competition focused on issues related to regional convergence, work developed recently has tried to address some of the shortcomings of the previous literature, developing a series of alternative approaches that center their attention on the assessment of regional economic performance. These alternative methodologies embrace a complex set of space-time interactions that take into account that a single region’s economic performance affects and is affected by other regions. In this context, and as an original contribution of this chapter, a spatial vector autoregressive (SpVAR) model for the Spanish regions during the period 1955 –2009 is presented. The SpVAR model considers spatial as well temporal lags of the variables. The estimated SpVAR is used to calculate impulse responses that provide insights about the effects of shocks to relative regional productive capacity on different regions. The empirical results suggest that the existence of trade linkages have had different significant impacts on the production shares of the 17 Spanish regions, but competition between regional economies prevails.

Original languageEnglish (US)
Title of host publicationGeography, Institutions and Regional Economic Performance
EditorsRiccardo Crescenzi, Marco Percoco
Number of pages26
ISBN (Electronic)9783642333958
ISBN (Print)9783642333941, 9783642430367
StatePublished - 2013

Publication series

NameAdvances in Spatial Science
ISSN (Print)1430-9602
ISSN (Electronic)2197-9375


  • Regional competition
  • Regional growth
  • Spatial VAR

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Economics and Econometrics


Dive into the research topics of 'Assessing Regional Economic Performance: Regional Competition in Spain Under a Spatial Vector Autoregressive Approach'. Together they form a unique fingerprint.

Cite this