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Semi-Parametric Spatial Auto-Covariance Models Of Regional Growth In Europe

Author

Listed:
  • Roberto BASILE

    (ISAE, Institute for Studies and Economic Analysis and University of Macerata,)

  • Bernard GRESS

    (Department of Economics, University of California Riverside, Riverside, California)

Abstract

We propose a semi-parametric spatial auto-covariance specification of the growth model to examine the growth behaviour of European regions in the period 1988-2000. This specification simultaneously takes account of the problems of non-linearities and spatial dependence. We obtain two main results. First, there is a trade-off between the identification of non-linearities and the estimation of the spatial parameters. Second, even when controlling for spatial dependence, there is evidence of strong non-linearity between regional growth and its predictors. In particular, the relation between growth and unemployment is negative but not globally linear: regions with an unemployment rate over a certain threshold appear to be 'locked' within an 'underdevelopment trap' with similar lower growth rates.

Suggested Citation

  • Roberto BASILE & Bernard GRESS, 2005. "Semi-Parametric Spatial Auto-Covariance Models Of Regional Growth In Europe," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 21, pages 93-118.
    • Handle: RePEc:tou:journl:v:21:y:2005:p:93-118
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Xu, Wan & Lambert, Dayton M., 2011. "Business Establishment Growth in the Appalachian Region, 2000-2007: An Application of Smooth Transition Spatial Process Models," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 43(3), pages 1-16, August.
    2. Zhiyong Chen & Jianbao Chen, 2022. "Bayesian analysis of partially linear, single-index, spatial autoregressive models," Computational Statistics, Springer, vol. 37(1), pages 327-353, March.
    3. Su, Liangjun, 2012. "Semiparametric GMM estimation of spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 167(2), pages 543-560.
    4. repec:asg:wpaper:1030 is not listed on IDEAS
    5. Pede, Valerien O. & Florax, Raymond J.G.M. & Lambert, Dayton M., 2014. "Spatial econometric STAR models: Lagrange multiplier tests, Monte Carlo simulations and an empirical application," Regional Science and Urban Economics, Elsevier, vol. 49(C), pages 118-128.
    6. Pede, Valerien O. & Florax, Raymond J.G.M. & Holt, Matthew T., 2009. "A Spatial Econometric Star Model With An Application To U.S. County Economic Growth, 1969–2003," Working papers 48117, Purdue University, Department of Agricultural Economics.
    7. 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).
    8. Zhang, Yuanqing & Sun, Yanqing, 2015. "Estimation of partially specified dynamic spatial panel data models with fixed-effects," Regional Science and Urban Economics, Elsevier, vol. 51(C), pages 37-46.
    9. Cem Ertur & Julie Le Gallo, 2008. "Regional Growth and Convergence: Heterogenous reaction versus interaction in spatial econometric approaches," Working Papers hal-00463274, HAL.
    10. Roberto Basile, 2008. "Regional economic growth in Europe: A semiparametric spatial dependence approach," Papers in Regional Science, Wiley Blackwell, vol. 87(4), pages 527-544, November.
    11. Rubo Zhao & Yixiang Tian & Ao Lei & Francis Boadu & Ze Ren, 2019. "The Effect of Local Government Debt on Regional Economic Growth in China: A Nonlinear Relationship Approach," Sustainability, MDPI, vol. 11(11), pages 1-22, May.
    12. Roberto Basile, 2014. "Regional productivity growth in Europe: a Schumpeterian perspective," Gecomplexity Discussion Paper Series 1, Action IS1104 "The EU in the new complex geography of economic systems: models, tools and policy evaluation", revised Nov 2014.
    13. Fang Lu & Jing Yang & Xuewen Lu, 2022. "One-step oracle procedure for semi-parametric spatial autoregressive model and its empirical application to Boston housing price data," Empirical Economics, Springer, vol. 62(6), pages 2645-2671, June.
    14. Sergio Rey, 2014. "Rank-based Markov chains for regional income distribution dynamics," Journal of Geographical Systems, Springer, vol. 16(2), pages 115-137, April.
    15. Marcos Sanso-Navarro & María Vera-Cabello, 2015. "Non-linearities in regional growth: A non-parametric approach," Papers in Regional Science, Wiley Blackwell, vol. 94, pages 19-38, November.
    16. Rosa Bernardini Papalia & Silvia Bertarelli, 2010. "Evaluating Total Factor Productivity Differences by a Mapping Structure in Growth Models," International Regional Science Review, , vol. 33(1), pages 31-59, January.
    17. Xu, Wan & Khachatryan, Hayk, 2013. "The Impact of Integrated Pest Management Practices on U.S. National Nursery Industry Annul Sales Revenue: An Application of Smooth Transition Spatial Autoregressive Models," 2013 Annual Meeting, February 2-5, 2013, Orlando, Florida 142961, Southern Agricultural Economics Association.
    18. Basile, Roberto & Durbán, María & Mínguez, Román & María Montero, Jose & Mur, Jesús, 2014. "Modeling regional economic dynamics: Spatial dependence, spatial heterogeneity and nonlinearities," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 229-245.

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    More about this item

    Keywords

    REGIONAL CONVERGENCE; EUROPE; SPATIAL ECONOMETRICS; MULTIPLE REGIMES; SEMI-PARAMETRIC MODELS;
    All these keywords.

    JEL classification:

    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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