Identifying nonlinear spatial dependence patterns by using non-parametric testsevidence for the European Union

  1. López Hernández, Fernando Antonio
  2. Artal Tur, Andrés
  3. Maté Sánchez de Val, Mari Luz
Journal:
Investigaciones Regionales = Journal of Regional Research

ISSN: 1695-7253,  2340-2717

Year of publication: 2011

Issue Title: Contributions to spatial econometrics

Issue: 21

Pages: 19-36

Type: Article

DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: Investigaciones Regionales = Journal of Regional Research

Abstract

Accounting for spatial structures in econometric studies is becoming an issue of special interest, given the presence of spatial dependence and spatial heterogeneity problems arising in data. Generally, researchers have been employing parametric tests for detecting spatial dependence structures: Moran’s I and LM tests in spatial regressions are the most popular approaches employed in literature.However, this approach remains misleading in the presence of nonlinear spatial structures, inducing important biases in the estimation of the parameters of the model. In this paper we illustrate that issue by applying three non-parametrical proposals when testing for spatial structure in data. Empirical findings for the regions of the European Union show important failures of traditional parametric tests if nonlinearities characterise geo-referenced data. Our results clearly recommend employing new families of tests, beyond parametrical ones, when working in such environments.

Bibliographic References

  • Anselin, L. (2010): «30 years of Spatial Econometrics», Papers in Regional Science, 89 (1): 3-25.
  • Anselin, L., and Florax, R. (1995): Small sample properties of tests for spatial dependence in regression models: Some further results. In: Anselin, L., Florax, R. (eds) New directions in spatial econometrics. Springer, Berlin.
  • Arbia, G.; Espab, G.; Giulianic, D., and Mazzitellic, A. (2010): «Detecting the existence of space-time clustering of firms», Regional Science and Urban Economics, 40 (5): 311-323.
  • Badinger, H.; Muller, W., and Tondl, G. (2004): «Regional Convergence in the European Union, 1985-1999: A Spatial Dynamic Panel Analysis», Regional Studies, 38: 241-253.
  • Basile, R. (2009): «Regional economic growth in Europe: A semiparametric spatial dependence approach», Papers in Regional Science, 87 (4): 527-565.
  • Basile, R., and Girardi, A. (2010): «Specialization and risk sharing in European regions», Journal of Economic Geography, 10 (5): 645-659.
  • Battisti, M., and Di Vaio, G. (2008): «A spatially filtered mixture of convergence regressions for EU regions, 1980-2002», Empirical Economics, 34: 105-121.
  • Brett, C, and Pinkse, J. (1997): «Those taxes are all over the map! A test for spatial independence of municipal tax rates in British Columbia», International Regional Science Review, 20: 131-151.
  • Dwass, M. (1957): «Modified randomization tests for nonparametric hypotheses», Annals of Mathematical Statistics, 2: 181-187.
  • Getis, A. (1990): «Screening for Spatial Dependence in Regression Analysis», Papers of the Regional Science Association, 69: 69-81.
  • Getis, A. (1995): «Spatial Filtering in a Regression Framework: Experiments on Regional Inequality, Government Expenditures, and Urban Crime», in Anselin, L., Florax, R. and Rey, S. (eds.): New Directions in Spatial Econometrics, Berlin, Springer.
  • Getis, A. and Ord, J. K. (1992): «The analysis of spatial association by use of distance statistics», Journal of Geographical Analysis, 24: 189-206.
  • Getis, A. and (1995): «Local spatial autocorrelation statistics: Distributional issues and an application», Journal of Geographical Analysis, 27: 286-305.
  • Getis, A. and Griffith, D. A. (2002): «Comparative spatial filtering in regression analysis», Geographical Analysis, 34 (2): 130-140.
  • Griffith, D. A. (1996): «Spatial Autocorrelation and Eigenfunctions of the Geographic Weights Matrix Accompanying Geo-Referenced Data», The Canadian Geographer, 40: 351-367.
  • Griffith, D. A. (2003): Spatial Autocorrelation and Spatial Filtering, Berlin, Springer.
  • Herrera, M. (2011): «Causality. Contributions to Spatial Econometrics», Ph. D. Thesis.University of Zaragoza.
  • Hinich, M. J., and Patterson, D. M. (1985): «Evidence of Nonlinearity in Daily Stock Returns», Journal of Business & Economic Statistics, 3 (1): 69-77.
  • Kang, H. (2010): «Detecting agglomeration processes using space-time clustering analyses», Annals of Regional Science, 45: 291-311.
  • Kulldorff, M., and Nagarwalla, N. (1995): «Spatial disease clusters: Detection and Inference», Statistics in Medicine, 14: 799-810.
  • Kulldorff, M.; Huang, L., and Konty, K. (2009): «A scan statistic for continuous data based on the normal probability model», International Journal of Health Geographies, 8, 58-73.
  • Le Sage, J., and Pace, R. K. (2009): Introduction to Spatial Econometrics, England, Taylor and Francis Group.
  • Lee, T. H.; White, H., and Granger, C. W. J. (1993): «Testing for neglected nonlinearity in time series models: A comparison of neural network methods and alternative tests», Journal of Econometrics, 56 (3): 269-290.
  • López, F A.; Mate, M. L., and Artal, A. (2011): Evaluating three proposals for testing independence in non linear spatial processes. Fundación de las Cajas de Ahorros. Working paper, n. 622/2011.
  • López, F. A.; Maulla, M.; Mur, J., and Ruiz, M. (2010): «A non-parametric spatial independence test using symbolic entropy», Regional Science and Urban Economics 40, 106-115.
  • López, F. A.; Maulla, M.; Mur, J., and Ruiz, M. (2011): «Four tests of independence in spatio-temporal data», Papers in Regional Science. 90 (3): 663-685.
  • Mayor, M., and López, A. (2008): «Spatial shift-share analysis versus spatial filtering: an application to Spanish employment data», Empirical Economics, 34: 123-142.
  • Meese, R. A., and Rose, A. K. (1990): «Nonlinear, Nonparametric, Nonessential Exchange Rate Estimation», The American Economic Review, 80 (2), Papers and Proceedings of the Hundred and Second Annual Meeting of the American Economic Association: 192-196.
  • Moran, P. A. (1948): «The interpretation of statistical maps», Journal of the Royal Statistical Society, Series B(10): 243-51.
  • Osland, L. (2010): «An Application of Spatial Econometrics in Relation to Hedonic House Price Modeling», Journal of Real Estate Research, 32 (3): 289-320.
  • Pinkse J., and Slade, M. E. (2010): «The future of spatial econometrics», Journal of Regional Science, 50: 103-117.
  • Pinkse, J. (1998): «A consistent non-parametric test for serial independence», Journal of Econometrics, 84 (2): 205-231.
  • Ruiz, M.; López, F. A., and Páez, A. (2010): «Testing for spatial association of qualitative data using symbolic dynamics», Journal of Geographical Systems, 12 (3): 281-309.
  • Solibakke, R (2005): «Non-linear dependence and conditional heteroscedasticity in stock returns evidence from the norwegian thinly traded equity market», European Journal of Finance, 11(2): 111-136.
  • Tango, T., and Takahashi, K. (2005): «A flexibly shaped spatial scan statistic for detecting clusters», International Journal of Health Geographies, 4 (1): 11.
  • Yiannakoulias, N.; Rosychuk, R., and Hodgson, J. (2007): «Adaptations for finding irregularly shaped disease clusters», International Journal of Health Geographies, 6 (1): 28.
Data source: Dialnet