Distribution Dynamics in the US. A Spatial Perspective

46 Pages Posted: 1 Feb 2016

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: January 25, 2016


It is quite common in cross-sectional convergence analyses that data exhibit strong spatial dependence. While the literature adopting the regression approach is now fully aware that neglecting this feature may lead to inaccurate results and has therefore suggested a number of statistical tools for addressing the issue, research is only at a very initial stage within the distribution dynamics approach. In particular, in the continuous state-space framework, a few authors opted for spatial pre-filtering the data in order to guarantee the statistical properties of the estimates. In this paper, we follow an alternative route that starts from the idea that spatial dependence is not just noise but can be a substantive element of the data generating process. In particular, we develop a tool that, building on a mean-bias adjustment procedure established in the literature, explicitly allows for spatial dependence in distribution dynamics analysis thus eliminating the need for pre-filtering. Using this tool, we then reconsider the evidence on convergence across US states.

Keywords: Distribution Dynamics, Nonparametric Smoothing, Spatial Dependence

JEL Classification: C14, C21

Suggested Citation

Gerolimetto, Margherita and Magrini, Stefano, Distribution Dynamics in the US. A Spatial Perspective (January 25, 2016). University Ca' Foscari of Venice, Dept. of Economics Research Paper Series No. 02/2016, Available at SSRN: https://ssrn.com/abstract=2725320 or http://dx.doi.org/10.2139/ssrn.2725320

Margherita Gerolimetto (Contact Author)

Ca Foscari University of Venice ( email )

Dorsoduro 3246
Venice, Veneto 30123

Stefano Magrini

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

Cannaregio 873
Venice, 30121

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