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

Ana Angulo, Jesús Mur, Javier Trivez


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.

Full Text:



Arellano, M. (2003) Panel data econometrics. Oxford University Press: Oxford.

Baltagi, B.H. and Li, D. (2004) Prediction in the panel data model with spatial correlation. In: Anselin, L., Florax, R.J.G.M. and Rey, S. (Eds) Advanced in spatial econometrics: methodology, tools and application. Springer-Verlag, Heidelberg (Germany): 283-295.

Baltagi, B.H. (2005) Econometric analysis of panel data. Third Edition. Wiley: Chichester.

Baltagi, B.H. and Li, D. (2006) Prediction in the panel data model with spatial correlation: the case of liquor, Spatial Economic Analysis, 1(2), 175-185.

Baltagi, B.H., Song, S.H., Jung, B.C. and Koh, W. (2006) Testing for serial correlation, spatial autocorrelation and random effects using panel data, Journal of Econometrics, 140, 5-51.

Baltagi, B.H., Bresson, G. and Pirotte, A. (2012) Forecasting with spatial panel data, Computational Statistics and Data Analysis, 56, 3381-3397.

Elhorst, J.P. (2003) Specification and Estimation of Spatial Panel Data Models, International Regional Sciences Review, 26, 244-268.

Elhorst, J.P. (2010) Spatial panel data models. In: Fischer, M.M. anf Getis, A. (Eds.). Handbook of Applied Spatial Analysis. Springer-Verlag: Berlin, 337-405.

Fingleton, B. and Palombim S. (2013) Spatial panel data estimation, counterfactual predictions and local economic resilience among British towns in the Victorian era, Regional Science and Urban Economics, 43, 649-660.

Hausman, J.A. (1978) Specification Tests in Econometrics, Econometrica, 46: 1251-1272.

Hsiao, C. (2003) Analysis of panel data (2nd Edition). Cambridge University Press: Cambridge.

Kapoor, M., Kelejian, H.H. and Prucha, I.R. (2007) Panel data models with spatially correlated error components, Journal of Econometrics, 140, 97-130.

Kelejian, H.H. and Prucha, I.R. (2002) 2SLS and OLS in a spatial autoregressive model with equal spatial weights, Regional Science and Urban Economics, 32, 691-707.

Kelejian, H.H., Prucha, I.R. and Yuzefovich, Y. (2006) Estimation problems in models with spatial weighting matrices which have blocks of equal elements, Journal of Regional Science, 46, 507-515.

Longhi, S. and Nijkamp, P. (2007) Forecasting regional labor market developments under spatial heterogeneity and spatial correlation, International Regional Science Review, 30, 100-119.

Pesaran, M.H. (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure, Econometrica, 74, 967-1012.

Wooldridge, J. (2002) Econometric Analysis of Cross Section and Panel Data. The MIT Press: Cambridge.

Yang, Z., Li, C., Tse, Y.K. (2006) Functional form and spatial dependence in spatial panels, Economics Letters, 91, 138-145.



  • There are currently no refbacks.

ISSN: 2254-4380