IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v68y2014i4p251-266.html
   My bibliography  Save this article

L-moments skewness and kurtosis as measures of regional convergence and cohesion

Author

Listed:
  • Katarzyna Kopczewska

Abstract

type="main"> The paper deals with the statistical modeling of convergence and cohesion over time with the use of kurtosis, skewness and L-moments. Changes in the shape of the distribution related to the spatial allocation of socio-economic phenomena are considered as an evidence of global shift, divergence or convergence. Cross-sectional time-series statistical modeling of variables of interest is to overpass the minors of econometric theoretical models of convergence and cohesion determinants. L-moments perform much more stable and interpretable than classical measures. Empirical evidence of panel data proves that one pure pattern (global shift, polarization or cohesion) rarely exists and joint analysis is required.

Suggested Citation

  • Katarzyna Kopczewska, 2014. "L-moments skewness and kurtosis as measures of regional convergence and cohesion," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(4), pages 251-266, November.
    • Handle: RePEc:bla:stanee:v:68:y:2014:i:4:p:251-266
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/stan.12031
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cowell, Frank A. & Flachaire, Emmanuel, 2007. "Income distribution and inequality measurement: The problem of extreme values," Journal of Econometrics, Elsevier, vol. 141(2), pages 1044-1072, December.
    2. Frank A. Cowell, 2008. "Income Distribution and Inequality," Chapters, in: John B. Davis & Wilfred Dolfsma (ed.), The Elgar Companion to Social Economics, chapter 13, Edward Elgar Publishing.
    3. Manganelli, Simone & White, Halbert & Kim, Tae-Hwan, 2008. "Modeling autoregressive conditional skewness and kurtosis with multi-quantile CAViaR," Working Paper Series 957, European Central Bank.
    4. Härdle, Wolfgang Karl & Blaskowitz, Oliver J. & Schmidt, Peter, 2004. "Skewness and Kurtosis Trades," Papers 2004,09, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    5. Sergio J. Rey, 2001. "Spatial Dependence in the Evolution of Regional Income Distributions," Urban/Regional 0105001, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kopczewska Katarzyna, 2019. "Can public intervention improve local public sector economic performance? The analysis of Special Economic Zones in Poland," Central European Economic Journal, Sciendo, vol. 6(53), pages 221-245, January.
    2. Milton Abdul Thorlie & Lixin Song & Muhammad Amin & Xiaoguang Wang, 2015. "Modeling and forecasting of stock index volatility with APARCH models under ordered restriction," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(3), pages 329-356, August.
    3. Mikos, Maria, 2019. "Zasięg dyfuzji bodźców gospodarczych– testowanie modelu rdzeń–peryferia w odniesieniu do kohezyjnej polityki regionalnej i lokalnej," Studia z Polityki Publicznej / Public Policy Studies, Warsaw School of Economics, vol. 6(2), pages 1-33, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Härdle, Wolfgang Karl & Schulz, Rainer & Xie, Taojun, 2019. "Cooling Measures and Housing Wealth: Evidence from Singapore," IRTG 1792 Discussion Papers 2019-001, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Guido Erreygers & Roselinde Kessels, 2017. "Socioeconomic Status and Health: A New Approach to the Measurement of Bivariate Inequality," IJERPH, MDPI, vol. 14(7), pages 1-23, June.
    3. Igor Fedotenkov, 2020. "A Review of More than One Hundred Pareto-Tail Index Estimators," Statistica, Department of Statistics, University of Bologna, vol. 80(3), pages 245-299.
    4. Antonio Abatemarco & Massimo Aria & Sergio Beraldo & Michela Collaro, 2023. "Measuring Access and Inequality of Access to Health Care: a Policy-Oriented Decomposition," CSEF Working Papers 666, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    5. Krittaya Sangkasem & Nattapong Puttanapong, 2022. "Analysis of spatial inequality using DMSP‐OLS nighttime‐light satellite imageries: A case study of Thailand," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(4), pages 828-849, August.
    6. Vladimir Hlasny & Paolo Verme, 2022. "The Impact of Top Incomes Biases on the Measurement of Inequality in the United States," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 749-788, August.
    7. Vladimir Hlasny, 2021. "Parametric representation of the top of income distributions: Options, historical evidence, and model selection," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1217-1256, September.
    8. Wolfgang Karl Hardle & Rainer Schulz & Taojun Sie, 2021. "Cooling Measures and Housing Wealth: Evidence from Singapore," Papers 2108.11915, arXiv.org.
    9. Vincent Hildebrand & Philippe Kerm, 2009. "Income inequality and self-rated health status: Evidence from the european community household panel," Demography, Springer;Population Association of America (PAA), vol. 46(4), pages 805-825, November.
    10. Sean Higgins & Nora Lustig & Andrea Vigorito, 2018. "The Rich Underreport their Income: Assessing Bias in Inequality Estimates and Correction Methods using Linked Survey and Tax Data," Working Papers 1808, Tulane University, Department of Economics.
    11. Michał Brzeziński, 2013. "Parametric Modelling of Income Distribution in Central and Eastern Europe," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 5(3), pages 207-230, September.
    12. Vladimir Hlasny & Paolo Verme, 2018. "Top Incomes and the Measurement of Inequality in Egypt," The World Bank Economic Review, World Bank, vol. 32(2), pages 428-455.
    13. Vladimir Hlasny & Paolo Verme, 2018. "Top Incomes and Inequality Measurement: A Comparative Analysis of Correction Methods Using the EU SILC Data," Econometrics, MDPI, vol. 6(2), pages 1-21, June.
    14. Stephen P. Jenkins, 2022. "Top-income adjustments and official statistics on income distribution: the case of the UK," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 151-168, March.
    15. Chiara Mussida & Maria Laura Parisi, 2016. "The effect of economic crisis on regional income inequality in Italy," DISCE - Quaderni del Dipartimento di Scienze Economiche e Sociali dises1614, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    16. Kaltenberg, Mary & Foster-McGregor, Neil, 2020. "The impact of automation on inequality across Europe," MERIT Working Papers 2020-009, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    17. Arthur Charpentier & Emmanuel Flachaire, 2022. "Pareto models for top incomes and wealth," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 1-25, March.
    18. Charpentier, Arthur & Flachaire, Emmanuel, 2015. "Log-Transform Kernel Density Estimation Of Income Distribution," L'Actualité Economique, Société Canadienne de Science Economique, vol. 91(1-2), pages 141-159, Mars-Juin.
    19. Christoph Lakner & Branko Milanovic, 2016. "Global Income Distribution: From the Fall of the Berlin Wall to the Great Recession," The World Bank Economic Review, World Bank, vol. 30(2), pages 203-232.
    20. Omoniyi B Alimi & David C Maré & Jacques Poot, 2020. "The effects of immigration and skills on urban income inequality in New Zealand: two decomposition approaches," RF Berlin - CReAM Discussion Paper Series 2023, Rockwool Foundation Berlin (RF Berlin) - Centre for Research and Analysis of Migration (CReAM).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:stanee:v:68:y:2014:i:4:p:251-266. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.