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Weighted samples, kernel density estimators and convergence

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Author Info

  • Francisco J. Goerlich Gisbert

Abstract

This note extends the standard kernel density estimator to the case of weighted samples in several ways. In the first place I consider the obvious extension by substituting the simple sum in the definition of the estimator by a weighted sum, but I also consider other alternatives of introducing weights, based on adaptive kernel density estimators, and consider the weights as indicators of the informational content of the observations and in this sense as signals of the local density of the data. All these ideas are shown using the Penn World Table in the context of the macroeconomic convergence issue. Copyright Springer-Verlag Berlin Heidelberg 2003

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File URL: http://hdl.handle.net/10.1007/s001810200134
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Bibliographic Info

Article provided by Springer in its journal Empirical Economics.

Volume (Year): 28 (2003)
Issue (Month): 2 (04)
Pages: 335-351

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Handle: RePEc:spr:empeco:v:28:y:2003:i:2:p:335-351

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Related research

Keywords: Key words: Weighted samples; survey data; regional data; kernel density estimates; convergence.; JEL classification: C00; C8.;

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Cited by:
  1. Juessen, Falko, 2009. "A Distribution Dynamics Approach to Regional GDP Convergence in Unified Germany," IZA Discussion Papers 4177, Institute for the Study of Labor (IZA).
  2. Benito , Juan Miguel & Ezcurra, Roberto, 2004. "Spatial disparities in the European Union: national and sectoral elements," Investigaciones Regionales, Asociación Española de Ciencia Regional, issue 4, pages 75-98.
  3. Goerlich, Francisco José & Mas, Matilde, 2004. "Three (Marginal?) Questions Regarding Convergence," MPRA Paper 15876, University Library of Munich, Germany, revised 2004.
  4. Hisham S. El-Osta, 2010. "Inequality decomposition of farm family living expenditures and the role of the life cycle," Agricultural Finance Review, Emerald Group Publishing, vol. 70(2), pages 245-266, August.

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