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How Reliable Are The Geographical Spatial Weights Matrices?

Author

Listed:
  • Davide FIASCHI

    () (Dipartimento di Economia e Management, Pisa, Italy)

  • Angela PARENTI

    () (IMT Institute for Advanced Studies Lucca)

Abstract

This paper shows how it is possible to estimate the interconnections between regions by a connectedness matrix recently proposed by Diebold and Yilmaz (2014), and discusses how the connectedness matrix is strictly related to the spatial weights matrix used in spatial econometrics. An empirical appli-cation using growth rate volatility of per capita GDP of 199 European NUTS2 regions (EU15) over the period 1981-2008 illustrates how our estimated connectedness matrix is not compatible with the most popular geographical weights matrices used in literature.

Suggested Citation

  • Davide FIASCHI & Angela PARENTI, 2014. "How Reliable Are The Geographical Spatial Weights Matrices?," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 40, pages 53-68.
  • Handle: RePEc:tou:journl:v:40:y:2014:p:53-68
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    File URL: http://region-developpement.univ-tln.fr/fr/pdf/R40/4_Fiaschi_Parenti2.pdf
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    References listed on IDEAS

    as
    1. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    2. Luisa Corrado & Bernard Fingleton, 2012. "Where Is The Economics In Spatial Econometrics?," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 210-239, May.
    3. BAUMONT, Catherine & ERTUR, Cem & LE GALLO, Julie, 2001. "A Spatial Econometric Analysis of Geographic Spillovers and Growth for European Regions, 1980-1995," LATEC - Document de travail - Economie (1991-2003) 2001-04, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
    4. Margaret M. McConnell & Gabriel Perez-Quiros, 2000. "Output fluctuations in the United States: what has changed since the early 1980s?," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
    5. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    6. Anselin, Luc, 2002. "Under the hood Issues in the specification and interpretation of spatial regression models," Agricultural Economics of Agricultural Economists, International Association of Agricultural Economists, vol. 27(3), November.
    7. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    8. Davide fiaschi & Angela Parenti, 2013. "An Estimate of the Degree of Interconnectedness between European Regions: A Bayesian Model Averaging Approach," Discussion Papers 2013/171, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    9. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
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    More about this item

    Keywords

    FIRST-ORDER CONTIGUITY; DISTANCE-BASED MATRIX; CONNECTEDNESS MATRIX; EUROPEAN REGIONS; NETWORK;

    JEL classification:

    • 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
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe

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