Convergence Analysis as Distribution Dynamics When Data are Spatially Dependent
22 Pages Posted: 14 Jul 2010
Date Written: July 13, 2010
Conditional distributions for the analysis of convergence are usually estimated using a standard kernel smoother but this is known to be biased. Hyndman et al. (1996) thus suggest a conditional density estimator with a mean function specified by a local polynomial smoother, i.e. one with better bias properties. However, even in this case, the estimated conditional mean might be incorrect when observations are spatially dependent. Consequently, in this paper we study per capita income inequalities among European Functional Regions and U.S. Metropolitan Statistical Areas through a distribution dynamics approach in which the conditional mean is estimated via a procedure that allows for spatial dependence (Gerolimetto and Magrini, 2009).
Keywords: Regional convergence, Distribution dynamics, Nonparametric smoothing, Spatial dependence
JEL Classification: R10, O40, C14, C21
Suggested Citation: Suggested Citation