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Weighted samples, kernel density estimators and convergence

Author

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  • 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

Suggested Citation

  • Francisco J. Goerlich Gisbert, 2003. "Weighted samples, kernel density estimators and convergence," Empirical Economics, Springer, vol. 28(2), pages 335-351, April.
    • Handle: RePEc:spr:empeco:v:28:y:2003:i:2:p:335-351
    DOI: 10.1007/s001810200134
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    Citations

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    Cited by:

    1. Jian-Xin Wu & Ling-Yun He, 2017. "The Distribution Dynamics of Carbon Dioxide Emissions Intensity across Chinese Provinces: A Weighted Approach," Sustainability, MDPI, vol. 9(1), pages 1-19, January.
    2. Arthur Charpentier & Ewen Gallic, 2016. "Kernel density estimation based on Ripley’s correction," Post-Print halshs-01238499, HAL.
    3. Falko Juessen, 2009. "A distribution dynamics approach to regional GDP convergence in unified Germany," Empirical Economics, Springer, vol. 37(3), pages 627-652, December.
    4. Shahram Amini & Michele Battisti & Christopher F. Parmeter, 2011. "Decomposing The Conditional Variance of Cross-Country Output," Working Papers 2011-18, University of Miami, Department of Economics.
    5. Daniel J. Henderson & Christopher F. Parmeter & R. Robert Russell, 2008. "Modes, weighted modes, and calibrated modes: evidence of clustering using modality tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 607-638.
    6. Benito , Juan Miguel & Ezcurra, Roberto, 2004. "Spatial disparities in the European Union: national and sectoral elements," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 4, pages 75-98.
    7. Adolfo Maza & José Villaverde & María Hierro, 2015. "Non- $$\hbox {CO}_2$$ CO 2 Generating Energy Shares in the World: Cross-Country Differences and Polarization," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 61(3), pages 319-343, July.
    8. Aparna Lolayekar & Pranab Mukhopadhyay, 2017. "Growth Convergence and Regional Inequality in India (1981–2012)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 15(2), pages 307-328, June.
    9. Francisco J. Goerlich & Matilde Mas, 2004. "Three (marginal?) questions regarding convergence," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 31(1), pages 25-38, February.
    10. 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 Limited, vol. 70(2), pages 245-266, August.

    More about this item

    Keywords

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

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General

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