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Misspecification in Linear Spatial Regression Models

Author

Listed:
  • Raymond J.G.M. Florax

    (Vrije Universiteit Amsterdam and University of Amsterdam)

  • Peter Nijkamp

    (Vrije Universiteit Amsterdam)

Abstract

Spatial effects are endemic in models based on spatially referenced data. The increased awareness of the relevance of spatial interactions, spatial externalities and networking effects among actors, evoked the area of spatial econometrics. Spatial econometrics focuses on the specification and estimation of regression models explicitly incorporating such spatial effects. The multidimensionality of spatial effects calls for misspecification tests and estimators that are notably different from techniques designed for the analysis of time series. With that in mind, we introduce the notion of spatial effects, referring to both heterogeneity and interdependence of phenomena occurring in two-dimensional space. Spatial autocorrelation or dependence can be detected by means of cross-correlation statistics in univariate as well as multivariate data settings. We review tools for exploratory spatial data analysis and misspecification tests for spatial effects in linear regress!ion models. A discussion of specification strategies and an overview of available software for spatial regression analysis, including their main functionalities, intend to give practitioners of spatial data analysis a head start.

Suggested Citation

  • Raymond J.G.M. Florax & Peter Nijkamp, 2003. "Misspecification in Linear Spatial Regression Models," Tinbergen Institute Discussion Papers 03-081/3, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20030081
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    References listed on IDEAS

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    1. Florax, Raymond J. G. M. & Folmer, Hendrik & Rey, Sergio J., 2003. "Specification searches in spatial econometrics: the relevance of Hendry's methodology," Regional Science and Urban Economics, Elsevier, vol. 33(5), pages 557-579, September.
    2. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    3. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
    4. H. Kelejian, Harry & Prucha, Ingmar R., 2001. "On the asymptotic distribution of the Moran I test statistic with applications," Journal of Econometrics, Elsevier, vol. 104(2), pages 219-257, September.
    5. Raymond J. G. M. Florax & Thomas Graaff, 2004. "The Performance of Diagnostic Tests for Spatial Dependence in Linear Regression Models: A Meta-Analysis of Simulation Studies," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 2, pages 29-65, Springer.
    6. Kelejian, Harry H. & Robinson, Dennis P., 1992. "Spatial autocorrelation : A new computationally simple test with an application to per capita county police expenditures," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 317-331, September.
    7. A Getis, 1991. "Spatial Interaction and Spatial Autocorrelation: A Cross-Product Approach," Environment and Planning A, , vol. 23(9), pages 1269-1277, September.
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    Cited by:

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    2. Cem Ertur & Thiaw Kalidou, 2005. "Growth and Spatial Dependence - The Mankiw, Romer and Weil model revisited," ERSA conference papers ersa05p660, European Regional Science Association.
    3. Micaela Antunes & Miguel Viegas & Celeste Varum & Carlos Pinho, 2020. "The Impact of Structural Funds on Regional Growth: A Panel Data Spatial Analysis," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 55(5), pages 312-319, September.
    4. Cho, Seong-Hoon & Kim, Seung Gyu & Clark, Christopher D. & Park, William M., 2007. "Spatial Analysis of Rural Economic Development Using a Locally Weighted Regression Model," Agricultural and Resource Economics Review, Cambridge University Press, vol. 36(1), pages 24-38, April.
    5. Gyubeom Park & Kichan Yoon & Munjae Lee, 2021. "Regional Factors Influencing Non-Take-Up for Social Support in Korea Using a Spatial Regression Model," SAGE Open, , vol. 11(4), pages 21582440211, December.
    6. Ou, Yunbei & Bailey, Nick & McArthur, David Philip & Zhao, Qunshan, 2024. "The Price Premium of Residential Energy Performance Certificates: A Scoping Review of the European Literature," OSF Preprints bm9xk, Center for Open Science.
    7. Modrego, F. & Celis, X. & Berdegué, J., 2008. "Polarización étnica de los ingresos rurales en el sur de Chile," Working papers 015, Rimisp Latin American Center for Rural Development.
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    9. Seong-Hoon Cho & Seung Gyu Kim & Dayton M. Lambert & Roland K. Roberts, 2013. "Impact of a Two-Rate Property Tax on Residential Densities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(3), pages 685-704.
    10. Stéphane VIROL (IERSO-IFReDE-GRES), 2006. "Three dimensions of regional integration process in Europe: an approach by spatial econometrics (In French)," Cahiers du GRES (2002-2009) 2006-16, Groupement de Recherches Economiques et Sociales.
    11. Anabela Ribeiro & Jorge Silva, 2011. "A spatial econometric analysis of cross-border accessibility and development in Portugal and Spain," ERSA conference papers ersa10p456, European Regional Science Association.
    12. Gjestland, Arnstein & McArthur, David Philip & Osland, Liv & Thorsen, Inge, 2014. "The suitability of hedonic models for cost-benefit analysis: Evidence from commuting flows," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 136-151.
    13. Liv Osland & Arnstein Gjestland & Inge Thorsen, 2020. "Measures of labour market accessibility. What can we learn from observed commuting patterns?," REGION, European Regional Science Association, vol. 7, pages 49-70.
    14. Kopetsch Thomas & Steffen Laura, 2020. "Ambulante Notfallbehandlung in der Gesetzlichen Krankenversicherung – Eine empirische Analyse," Zeitschrift für Wirtschaftspolitik, De Gruyter, vol. 69(3), pages 203-231, December.
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    More about this item

    Keywords

    spatial econometrics; spatial autocorrelation; spatial heterogeneity; misspecification testing;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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