The Scan-LM to Test Instability in the Constant Coefficient of Spatial Autoregressive Models

  1. Fernando A. López Hernández 1
  2. Román Mínguez Salido
  1. 1 Universidad Politécnica de Cartagena
    info

    Universidad Politécnica de Cartagena

    Cartagena, España

    ROR https://ror.org/02k5kx966

Revista:
Economía

ISSN: 0254-4415

Año de publicación: 2021

Volumen: 44

Número: 87

Páginas: 74-88

Tipo: Artículo

DOI: 10.18800/ECONOMIA.202101.005 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Economía

Objetivos de desarrollo sostenible

Resumen

This paper presents a test based on the principle of Lagrange Multipliers to identify spatial instability in the constant coefficient of regression models including substantive spatial dependence. The test has been adapted to the Scan methodology. Its main advantage is that it identifies areas with differential behavior without the need to provide information about their location, shape, or size. The study shows the utility of the test, reconsidering the results obtained by Mur et al.(2008) about instability in the distribution of per capita income in European regions.

Referencias bibliográficas

  • Abreu, M., De Groot, H., and Florax, R. (2005). Space and Growth: A Survey of Empirical Evidence and Methods.Région et Développement 21, 13–44.
  • Basile, R., Durbán, M., Mínguez, R., Montero, J. M., and Mur, J. (2014). Modeling regionaleconomic dynamics: Spatial dependence, spatial heterogeneity and nonlinearities. Journal of Economic Dynamics and Control 48, 229–245.
  • Billé, A. G., Benedetti, R., and Postiglione, P. (2017). A two-step approach to account forunobserved spatial heterogeneity.Spatial Economic Analysis 12(4), 452–471.
  • Brunsdon, C., Fotheringham, A. S., and Charlton, M. E. (1996). Geographically Weighted Re-gression: A Method for Exploring Spatial Nonstationarity.Geographical Analysis 28(4),281–298.
  • Chasco, C., Le Gallo, J., and López, F. A. (2018). A scan test for spatial groupwise heteroscedas-ticity in cross-sectional models with an application on houses prices in Madrid.RegionalScience and Urban Economics 68, 226–238.
  • Chasco C., López F. A., and Le Gallo, J. (2020). The Spatial Structure of Housing Prices inMadrid: Evidence from Spatio-Temporal Scan Statistics. In J. Glaz and M. V. Koutras(Eds.),Handbook Of Scan Statistics(1–19). New York: Springer.
  • Cucala, L. (2016). Scan Statistics for Detecting High-Variance Clusters.Journal of Probabilityand Statistics 2016, Article ID 7591680, 8 pages.
  • Fischer, M. M., and Stirböck, C. (2006). Pan-European regional income growth and club-convergence.The Annals of Regional Science 40(4), 693–721
  • Fotheringham, A., Charlton, M., and Brunsdon, C. (1999). Geographically weighted regression.A natural evolution of the expansion method for spatial data analysis.Environment andPlanning A 30(11), 1905–1927.
  • Getis, A., and Ord, J. K. (1992). The Analysis of Spatial Association by Use of Distance Statistics.Geographical Analysis 24(3), 189–206.
  • Griffith, D. (2003).Spatial Autocorrelation and Spatial Filtering: Gaining UnderstandingThrough Theory and Scientific Visualization. Berlin, Heidelberg: Springer-Verlag.
  • Kulldorff, M., and Nagarwalla, N. (1995). Spatial Disease Clusters: Detection and Inference.Statistics in Medicine 14(8), 799–810.
  • Lauridsen, J., and Kosfeld, R. (2011). Spurious spatial regression and heteroscedasticity.Journalof Spatial Science 56(1), 59–72.
  • Le Gallo, J., and Dall’Erba, S. (2006). Evaluating the Temporal and Spatial Heterogeneity of theEuropean Convergence Process, 1980–1999.Journal of Regional Science 46(2), 269–288.
  • Le Gallo, J., López, F. A., and Chasco, C. (2020). Testing for spatial group-wise heteroskedas-ticity in spatial autocorrelation regression models: Lagrange multiplier scan tests.TheAnnals of Regional Science 64(2), 287–312.
  • Lee, J., Gangnon, R. E., and Zhu, J. (2017). Cluster detection of spatial regression coefficients.Statistics in Medicine 36(7), 1118–1133.
  • López, F. A., Chasco, C., and Le Gallo, J. (2015). Exploring scan methods to test spatial structure with an application to housing prices in Madrid. Papers in Regional Science 94(2), 317–346.
  • Mur, J., López, F. A., and Angulo, A. (2008). Symptoms of Instability in Models of SpatialDependence.Geographical Analysis 40(2), 189–211.
  • Ramajo, J., Márquez, M. A., Hewings, G. J., and Salinas, M. M. (2008). Spatial heterogene-ity and interregional spillovers in the European Union: Do cohesion policies encourageconvergence across regions?European Economic Review 52(3), 551–567.
  • Tango, T., and Takahashi, K. (2005). A flexibly shaped spatial scan statistic for detecting clusters.International Journal of Health Geographics 4, 11.Zhang, Z., Assunção, R., Kulldorff, M. (2010). Spatial Scan Statistics Adjusted for MultipleClusters.Journal of Probability and Statistics 2015, 1–11.
Fuente de los datos: Dialnet