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Measure of the resilience to Spanish economic crisis: the role of specialization

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  • Ana Angulo
  • Jesús Mur
  • Javier Trivez

Abstract

Forecasting regional variables provides very important information for political, institutional and economic agents. In this paper, we use predictions from spatial panel data models to evaluate regional resilience to the present economic crisis in term of annual growth rate of employment. Furthermore, we evaluate whether specialization plays a significant role in the degree of resilience to the economic crisis suffered in Spain from 2007. Results show that while specialization on construction and non-market services declines resilience to the crisis, specialization on energy and manufacturing or distribution, transport and common services enlarges the availability of returning to his pre-shock growth path.

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  • 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.
    • Handle: RePEc:ove:journl:aid:10419
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    File URL: https://reunido.uniovi.es/index.php/EBL/article/view/10419
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    1. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    2. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    3. Manfred M. Fischer & Arthur Getis (ed.), 2010. "Handbook of Applied Spatial Analysis," Springer Books, Springer, number 978-3-642-03647-7, April.
    4. Kelejian, Harry H. & Prucha, Ingmar R., 2002. "2SLS and OLS in a spatial autoregressive model with equal spatial weights," Regional Science and Urban Economics, Elsevier, vol. 32(6), pages 691-707, November.
    5. Baltagi, Badi H. & Heun Song, Seuck & Cheol Jung, Byoung & Koh, Won, 2007. "Testing for serial correlation, spatial autocorrelation and random effects using panel data," Journal of Econometrics, Elsevier, vol. 140(1), pages 5-51, September.
    6. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2012. "Forecasting with spatial panel data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3381-3397.
    7. 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.
    8. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291.
    9. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    10. 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.
    11. Yang, Zhenlin & Li, Chenwei & Tse, Y.K., 2006. "Functional form and spatial dependence in dynamic panels," Economics Letters, Elsevier, vol. 91(1), pages 138-145, April.
    12. J. Paul Elhorst, 2003. "Specification and Estimation of Spatial Panel Data Models," International Regional Science Review, , vol. 26(3), pages 244-268, July.
    13. Fingleton, Bernard & Palombi, Silvia, 2013. "Spatial panel data estimation, counterfactual predictions, and local economic resilience among British towns in the Victorian era," Regional Science and Urban Economics, Elsevier, vol. 43(4), pages 649-660.
    14. Simonetta Longhi & Peter Nijkamp, 2007. "Forecasting Regional Labor Market Developments under Spatial Autocorrelation," International Regional Science Review, , vol. 30(2), pages 100-119, April.
    15. 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.
    16. Harry H. Kelejian & Ingmar R. Prucha & Yevgeny Yuzefovich, 2006. "Estimation Problems In Models With Spatial Weighting Matrices Which Have Blocks Of Equal Elements," Journal of Regional Science, Wiley Blackwell, vol. 46(3), pages 507-515, August.
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    Cited by:

    1. Liangang Li & Pingyu Zhang & Xin Li, 2019. "Regional Economic Resilience of the Old Industrial Bases in China—A Case Study of Liaoning Province," Sustainability, MDPI, vol. 11(3), pages 1-14, January.
    2. Ferran Navinés & José Pérez-Montiel & Carles Manera & Javier Franconetti, 2023. "Ranking the Spanish regions according to their resilience capacity during 1965–2011," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 71(2), pages 415-435, October.
    3. Sonja Brlečić Valčić & Anita Peša & Dijana Čičin-Šain, 2022. "Analysis of the Eurozone’s Resilience to Crises and Disturbances in the Context of EU Development Strategies—Contemporary Approach Using Anfis," Sustainability, MDPI, vol. 14(23), pages 1-28, November.
    4. Marek Obrębalski & Marek Walesiak, 2015. "Functional structure of Polish regions in the period 2004-2013 – measurement via HHI Index, Florence’s coefficient of localization and cluster analysis," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(2), pages 223-242, June.
    5. Marek Obrębalski & Marek Walesiak, 2015. "Functional Structure Of Polish Regions In The Period 2004-2013 – Measurement Via Hhi Index, Florence’S Coefficient Of Localization And Cluster Analysis," Statistics in Transition New Series, Polish Statistical Association, vol. 16(2), pages 223-242, June.
    6. Obrębalski Marek & Walesiak Marek, 2015. "Functional Structure of Polish Regions in the Period 2004-2013 – Measurement Via HHI Index, Florence’s Coefficient of Localization and Cluster Analysis," Statistics in Transition New Series, Statistics Poland, vol. 16(2), pages 223-242, June.

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