Spatial HAC Estimator: Analysis of Convergence of European Regions

24 Pages Posted: 22 Dec 2008

See all articles by Oleksandr Shepotylo

Oleksandr Shepotylo

Aston University - Aston Business School

Date Written: December 22, 2008


This paper applies a non-parametric heteroscedasticity 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 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 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 effect 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.

Keywords: convergence, spatial econometrics, regional economics, EU

JEL Classification: C1, R1

Suggested Citation

Shepotylo, Oleksandr, Spatial HAC Estimator: Analysis of Convergence of European Regions (December 22, 2008). Available at SSRN: or

Oleksandr Shepotylo (Contact Author)

Aston University - Aston Business School ( email )

Aston Triangle
Birmingham, B47ET
United Kingdom

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