This paper applies a nonparametric heteroskedasticity and autocorrelation consistent (HAC) estimator of error terms in the context of the spatial autoregressive model of GDP per capita convergence of European regions at NUTS 2 level. By introducing the spatial dimension, it looks at how the equilibrium distribution of GDP per capita of EU regions evolves both in time and space dimensions. Results demonstrate that the global spatial spillovers of growth rates make an important contribution to the process of convergence by reinforcing the economic growth of neighboring regions. Results are even more pronounced when the convergence in wage per worker is considered. The choice of kernel functions does not significantly affect the estimation of the variance-covariance matrix, while the choice of the bandwidth parameter is quite important. Finally, results are sensitive to the weighting matrix specification, and further research is needed to give a more rigorous justification for the selection of the weighting matrix.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Publisher Info
Paper provided by Kyiv School of Economics in its series Discussion Papers with number
15.
Length: Date of creation: Feb 2009 Date of revision: Handle: RePEc:kse:dpaper:15
Note: Under review in Regional Studies Contact details of provider: Postal: 13 Yakira Str, 04119 Kyiv Phone: (38-044)492-8012 Fax: (38-044)492-8011 Email: Web page: http://www.kse.org.ua/ More information through EDIRC
For technical questions regarding this item, or to correct its listing, contact: (Andriy Zapechelnyuk).