IDEAS home Printed from https://ideas.repec.org/p/wiw/wiwrsa/ersa04p524.html
   My bibliography  Save this paper

Convergence in per-capita GDP across European regions using panel data models extended to spatial autocorrelation effects

Author

Listed:
  • Giuseppe Arbia
  • Gianfranco Piras

Abstract

This paper studies the convergence of per capita GDP across European regions over a fairly long period. Most of the works are based on either cross-sectional or fixed-effects estimates. We propose the estimation of convergence in per capita GDP across European regions by making use of panel-data models extended to include spatial error autocorrelation and spatially lagged dependent variable (Anselin,1988;Elhorst,2002). This will allow us to extend the traditional ß convergence model to include a rigorous treatment of the spatial correlation among the intercept terms. A spatial analysis of such intercept terms will also be performed in order to shed light on the concept spatially conditional convergence.

Suggested Citation

  • Giuseppe Arbia & Gianfranco Piras, 2004. "Convergence in per-capita GDP across European regions using panel data models extended to spatial autocorrelation effects," ERSA conference papers ersa04p524, European Regional Science Association.
    • Handle: RePEc:wiw:wiwrsa:ersa04p524
    as

    Download full text from publisher

    File URL: https://www-sre.wu.ac.at/ersa/ersaconfs/ersa04/PDF/524.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Valentina Meliciani & Franco Peracchi, 2009. "Convergence in per-capita GDP across European regions: a reappraisal," Studies in Empirical Economics, in: Giuseppe Arbia & Badi H. Baltagi (ed.), Spatial Econometrics, pages 203-222, Springer.
    2. Elhorst, J. Paul, 2001. "Panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable," Research Report 01C05, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    3. Elhorst, J.P., 2000. "Dynamic models in space and time," Research Report 00C16, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    4. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 175-185.
    5. Holtz-Eakin, Douglas, 1994. "Public-Sector Capital and the Productivity Puzzle," The Review of Economics and Statistics, MIT Press, vol. 76(1), pages 12-21, February.
    6. repec:dgr:rugsom:01c05 is not listed on IDEAS
    7. Case, Anne C, 1991. "Spatial Patterns in Household Demand," Econometrica, Econometric Society, vol. 59(4), pages 953-965, July.
    8. Giuseppe Arbia & Roberto Basile & Mirella Salvatore, 2002. "Regional Convergence in Italy 1951-199: A Spatial Econometric Perspective," ISAE Working Papers 29, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    9. Badi H. Baltagi & Dong Li, 2004. "Prediction in the Panel Data Model with Spatial Correlation," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 13, pages 283-295, Springer.
    10. J. Paul Elhorst, 2003. "Specification and Estimation of Spatial Panel Data Models," International Regional Science Review, , vol. 26(3), pages 244-268, July.
    11. Quah, Danny T., 1996. "Empirics for economic growth and convergence," European Economic Review, Elsevier, vol. 40(6), pages 1353-1375, June.
    12. Nazrul Islam, 1995. "Growth Empirics: A Panel Data Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(4), pages 1127-1170.
    Full references (including those not matched with items on IDEAS)

    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. Mark V. JANIKAS & Sergio J. REY, 2008. "On The Relationships Between Spatial Clustering, Inequality, And Economic Growth In The United States : 1969-2000," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 27, pages 13-34.
    2. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    3. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    4. Hujer Reinhard & Rodrigues Paulo J. M. & Wolf Katja, 2008. "Dynamic Panel Data Models with Spatial Correlation," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(5-6), pages 612-629, October.
    5. Riccardo Corradini, 2006. "Advanced estimates of regional accounts: an alternative approach by spatial panels," Computing in Economics and Finance 2006 287, Society for Computational Economics.
    6. Massimiliano Agovino & Antonio Garofalo, 2013. "Dipendenza spaziale contemporanea e non contemporanea nei tassi di disoccupazione: un tentativo di analisi empirica dei dati provinciali italiani," RIVISTA DI ECONOMIA E STATISTICA DEL TERRITORIO, FrancoAngeli Editore, vol. 2013(3), pages 45-82.
    7. Lee, Lung-fei & Yu, Jihai, 2010. "Some recent developments in spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 255-271, September.
    8. Ana Angulo & Jesús Mur & Javier Trivez, 2014. "Measure of the resilience to Spanish economic crisis: the role of specialization," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 263-275.
    9. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    10. Ana Angulo & Jesús Mur & Javier Trívez, 2013. "Forecasting heterogeneous regional data: the case of European employment," ERSA conference papers ersa13p953, European Regional Science Association.
    11. Cristina D. Checherita, 2009. "Variations on economic convergence: The case of the United States," Papers in Regional Science, Wiley Blackwell, vol. 88(2), pages 259-278, June.
    12. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    13. Charles Plaigin, 2009. "Exploratory study on the presence of cultural and institutional growth spillovers," DULBEA Working Papers 09-03.RS, ULB -- Universite Libre de Bruxelles.
    14. Harald Badinger & Peter Egger, 2009. "Estimation of Higher-Order Spatial Autoregressive Panel Data Error Component Models," CESifo Working Paper Series 2556, CESifo.
    15. Pei Li, 2008. "Metropolitan economic growth and spatial dependence: Evidence from a panel of China," Psychometrika, Springer;The Psychometric Society, vol. 3(2), pages 277-295, June.
    16. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
    17. Tobias Hagen & Philipp Mohl, 2011. "Econometric Evaluation of EU Cohesion Policy: A Survey," Chapters, in: Miroslav N. Jovanović (ed.), International Handbook on the Economics of Integration, Volume III, chapter 16, Edward Elgar Publishing.
    18. Maria ABREU & Henri L.F. DE GROOT & Raymond J.G.M. FLORAX, 2005. "Space And Growth: A Survey Of Empirical Evidence And Methods," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 21, pages 13-44.
    19. Baltagi, Badi H. & Pirotte, Alain, 2014. "Prediction in a spatial nested error components panel data model," International Journal of Forecasting, Elsevier, vol. 30(3), pages 407-414.
    20. Giuseppe Arbia & Roberto Basile & Gianfranco Piras, 2005. "Using Spatial Panel Data in Modelling Regional Growth and Convergence," ISAE Working Papers 55, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).

    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:wiw:wiwrsa:ersa04p524. 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: Gunther Maier (email available below). General contact details of provider: http://www.ersa.org .

    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.