Convergence Analysis as Distribution Dynamics When Data are Spatially Dependent

22 Pages Posted: 14 Jul 2010

See all articles by Margherita Gerolimetto

Margherita Gerolimetto

Ca Foscari University of Venice

Stefano Magrini

Ca Foscari University of Venice - Dipartimento di Economia

Date Written: July 13, 2010

Abstract

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

Gerolimetto, Margherita and Magrini, Stefano, Convergence Analysis as Distribution Dynamics When Data are Spatially Dependent (July 13, 2010). University Ca' Foscari of Venice, Dept. of Economics Research Paper Series No. 12_10, Available at SSRN: https://ssrn.com/abstract=1639345 or http://dx.doi.org/10.2139/ssrn.1639345

Margherita Gerolimetto

Ca Foscari University of Venice ( email )

Dorsoduro 3246
Venice, Veneto 30123
Italy

Stefano Magrini (Contact Author)

Ca Foscari University of Venice - Dipartimento di Economia ( email )

Cannaregio 873
Venice, 30121
Italy

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