Una propuesta metodológica para el análisis gráfico de series temporales regionalesuna aplicación a las tasas de paro provinciales en España

  1. Ferrán Aranaz, Magdalena
  2. Escot Mangas, Lorenzo
Journal:
Investigaciones Regionales = Journal of Regional Research

ISSN: 1695-7253 2340-2717

Year of publication: 2019

Issue: 43

Pages: 57-81

Type: Article

More publications in: Investigaciones Regionales = Journal of Regional Research

Abstract

This paper presents a methodology for longitudinal comparative analysis of regional time series. This methodology is integrated by the sheaf of straight lines methodology, proposed by Ferrán (2011), and the multidimensional scaling analysis. The interest of this methodology is that it visualizes the similarities and differences between the dynamics of each of the regions. We present this methodology through an application to the provincial study of unemployment rates in Spain over the period 1991-2018. The results of the analysis confirm the relevance of spatial components in the evolution of the unemployment elasticity over the economic cycle.

Bibliographic References

  • Alonso-Villar, O.; Del Río, C. y Toharia, L. (2009): “Un análisis espacial del desempleo por municipios”, Revista de Economía Aplicada, 49 (XXVII), páginas 47-80.
  • Andrienko, N.; Andrienko, G. y Gatalsky, P. (2003): “Exploratory spatio-temporal visualization: an analytical review”, Journal of Visual Languages & Computing, 14: 503-541.
  • Bande, R.; Fernández, M.; y Montuenga V. (2008): “Regional unemployment in Spain: Disparities, business cycle and wage setting”. Labour Economics, Volume 15, Issue 5, October 2008, pp. 885-914.
  • Bande, R. and Karanassou, M. (2009): “Labour market flexibility and regional unemployment rate dynamics: Spain 1980-1995”. Papers in Re fonal Science, 88: 181-207.
  • Bande R, Karanassou, M. (2013) “The NRU and the evolution of regional disparities in Spanish unemployment”.Urban Studies 50(10):2044-2062.
  • Bande, R, Karanassou, M. (2014): “Spanish regional unemployment revisited: the role of capital accumulation”. Regional Studies. Volume 48, Issue 11, 2014.
  • Borg, I. and Groenen, P. (2005): Modern Multidimensional Scaling: Theory and Applications (2nd ed.). New York: Springer-Verlag.
  • Cueto Iglesias, M.B., Mayor Fernández, M y Suarez Cano, P. (2017): La resiliencia de las regiones españolas, después de la gran recesión. Consejo Económico y Social del Principado de Asturias. Colección de Estudios, núm. 20.
  • Fernández Macho, F.J. (dir.) (1997): Cointegración y convergencia en la Unión Europea,Colección Economía, Servicio Editorial de la Universidad del País Vasco.
  • Ferrán Aranaz, M. (2011): Una metodología de minería de datos para la agupación de series temporales: aplicación al sector de la construcción residencial, Universidad Complutense de Madrid, Tesis Doctoral.
  • Ferrán Aranaz, M. (2013): “El haz de rectas para la comparación gráfica de series temporales geográficas”, Estadística Española, 181: 123-148.
  • García Cintado,A.; Romero Avila, D.; y Usabiaga, C. (2015): “Can the hysteresis hypothesis in Spanish regional unemployment be beaten? New evidence from unit root tests with breaks”. Economic Modelling, Volume 47, June 2015, Pages 244-252.
  • Gelman, A. y Unwin, A. (2011): “Visualization, Graphics and Statistics”, Statistical Computing & Graphics Newsletter, 22: 9-12.
  • Hochheiser, H. y Shneiderman, B. (2001): “Interactive exploration of time series data”, In: The 4th International conference on Discovery Science (Washington, DC), Springer-Verlag, Berlin, 441-446.
  • Kosara, R (2011): “Visualization: is more than Pictures!”, Statistical Computing & Graphics Newsletter, 22: 5-8.
  • Liao, T.W. (2005): “Clustering of time series data - a survey”, Pattern Recognition. 38: 1857-1874.
  • Lin, J.; Keogh, E. y Lonardi, S. (2005): “Visualizing and discovering non-trivial patterns in large time series databases”. Information Visualization, 4: 61-82.
  • López-Bazo, E.; Del Barrio, T. y Artis, M. (2002): “The Regional Distribution of Spanish Unemployment. A Spatial Analysis”, Papers in Regional Science, 81, páginas 365- 389.
  • López-Bazo, E.; Del Barrio, T. y Artis, M. (2005): “Geographical Distribution of Unemployment in Spain”, Regional Studies, 39 (3), pp 305-318.
  • López-Bazo E, Motellón E (2013): “The regional distribution of unemployment: what do micro-data tell us?”, Papers in Regional Science 92(2), pp383-405.
  • Mead, A (1992): “Review of the Development of Multidimensional Scaling Methods”, Journal of the Royal Statistical Society. Series D (The Statistician), Vol. 41, No. 1, pp. 27-39.
  • Real Deus, J.E. y Varela Mallou, J (2003): “Esclamiento multidimensional” en Lévy Mangin, J.P y Varela Mallou, J (dirs.): Análisis multivariable para las Ciencias Sociales, Pearson Prentice-Hall, Madrid, pp 451-505.
  • Sala, H. y Trivín, P. (2014): “Labour market dynamics in Spanish regions: evaluating asymmetries in troublesome times”. SERIEs, August 2014, Volume 5, Issue 2, pp 197-221.
  • Suriñach, J; Artis, M.; López, E.; y Sansó, A. (1995): Análisis económico regional, nociones básicas de la Teoría de la Cointegración. Antoni Bosch Editor.
  • Tufte, E.R. (2001): The visual display of quantitative information, Cheshire, Conn: Graphic Press.
  • Van Wijk, J.J. y van Selow, E.R. (1999): “Cluster and calendar based visualization of time series data”, In: 1999 IEEE Symposium on Information Visualization (San Francisco, CA), 4-9.
  • Viñuela Jiménez, A; Rubiera Morollón, F.; y Cueto Iglesias, B. (2012): “Espacio y empleabilidad. ¿Importa el concepto de región?”, Información Comercial Española, Revista de Economía, 865: 155-167.
  • Viñuela Jiménez, A.; Rubiera Morollón, F.; y Fernández Vázquez, E.(2014): “Applying economic-based analytical regions: A study of the spatial distribution of employment in Spain”, The Annals of Regional Science, January 2014, Volume 52, Issue 1, pp .87-102.
  • Weber, M.; Alexa, M. y Muller, W. (2001): “Visualizing time series on spirals”, In: 2001 IEEE Symposium on Information Visualization (San Diego, CA), 7-14.
  • Zhao, J; Chevalier, F.; Pietriga, E. y Balakrishnan, R. (2011): “Exploratory Analysis of Time-Series with ChronoLenses”, IEEE Transaction on visualization and computers graphics, 77(12): 2422-2431.
  • Zhao, Y (2015): R and Data Mining: Examples and Case Studies. Academic Press, Elsevier.