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Distribution dynamics: a spatial perspective

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  • Margherita Gerolimetto
  • Stefano Magrini

Abstract

It is quite common in cross-sectional convergence analyses that data exhibit spatial dependence. Within the literature adopting the distribution dynamics approach, authors typically opt for spatial prefiltering. We follow an alternative route and propose a procedure based on an estimate of the mean function of a conditional density for which we develop a two-stage non-parametric estimator that allows for spatial dependence estimated via a spline estimator of the spatial correlation function. The finite sample performance of this estimator is assessed via Monte Carlo simulations. We apply the procedure that incorporates the proposed spatial non-parametric estimator to data on per capita personal income in US states and metropolitan statistical areas.

Suggested Citation

  • Margherita Gerolimetto & Stefano Magrini, 2023. "Distribution dynamics: a spatial perspective," Spatial Economic Analysis, Taylor & Francis Journals, vol. 18(1), pages 64-88, January.
    • Handle: RePEc:taf:specan:v:18:y:2023:i:1:p:64-88
    DOI: 10.1080/17421772.2022.2095005
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