Convergence analysis as distribution dynamics when data are spatially dependent
AbstractConditional 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).
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Bibliographic InfoPaper provided by Department of Economics, University of Venice "Ca' Foscari" in its series Working Papers with number 2010_12.
Date of creation: 2010
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Regional convergence; Distribution dynamics; Nonparametric smoothing; Spatial dependence;
Find related papers by JEL classification:
- R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General
- O40 - Economic Development, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-07-24 (All new papers)
- NEP-ECM-2010-07-24 (Econometrics)
- NEP-GEO-2010-07-24 (Economic Geography)
- NEP-URE-2010-07-24 (Urban & Real Estate Economics)
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