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Spatial Distribution Dynamics

Stefano Magrini () and Margherita Gerolimetto ()

ERSA conference papers from European Regional Science Association

Abstract: It is quite common in convergence analyses across regions 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 the mean-bias adjustment procedure proposed by Hyndman et al. (1996), 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 regional economies in the US.

Keywords: immigration; convergence; distribution dynamics; spatial effects (search for similar items in EconPapers)
JEL-codes: C14 C21 J61 O47 (search for similar items in EconPapers)
Date: 2015-10
New Economics Papers: this item is included in nep-ecm
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