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Estimating spatial weighting matrices in cross-regressive models by entropy techniques

  • Esteban Fernandez-Vazquez

    ()

The traditional approach to estimate spatial models bases on a preconceived spatial weights matrix to measure spatial interaction among locations. The a priori assumptions used to define this matrix are supposed to be in line with the “true” spatial relationships among the locations of the dataset. Another possibility consists on using some information present on the sample data to specify an empirical matrix of spatial weights. In this paper we propose to estimate spatial cross-regressive models by generalized maximum entropy (GME). This technique allows combing assumptions about the spatial interconnections among the locations studied with information from the sample data. Hence, the spatial component of the model estimated by the techniques proposed is not just preconceived but it allows incorporating empirical information. We compare some traditional methodologies with the proposed GME estimator by means of Monte Carlo simulations in several scenarios and show that the entropy-based estimation techniques can outperform traditional approaches. An empirical case is also studied in order to illustrate the implementation of the proposed techniques for a real-world example.

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Paper provided by European Regional Science Association in its series ERSA conference papers with number ersa10p503.

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Date of creation: Sep 2011
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Handle: RePEc:wiw:wiwrsa:ersa10p503
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  1. Arndt, Channing & Robinson, Sherman & Tarp, Finn, 1999. "Parameter estimation for a computable general equilibrium model: a maximum entropy approach," TMD discussion papers 40, International Food Policy Research Institute (IFPRI).
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  4. Esteban Fernández-Vázquez & Matías Mayor-Fernández & Jorge Rodríguez-Vález, 2009. "Estimating Spatial Autoregressive Models by GME-GCE Techniques," International Regional Science Review, , vol. 32(2), pages 148-172, April.
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  7. Anselin, Luc, 2002. "Under the hood Issues in the specification and interpretation of spatial regression models," Agricultural Economics of Agricultural Economists, International Association of Agricultural Economists, vol. 27(3), November.
  8. Rosina Moreno & Enrique López-Bazo, 2007. "Returns to Local and Transport Infrastructure under Regional Spillovers," International Regional Science Review, , vol. 30(1), pages 47-71, January.
  9. Enrique López-Bazo & Esther Vayá & Manuel Artís, 2004. "Regional Externalities And Growth: Evidence From European Regions," Journal of Regional Science, Wiley Blackwell, vol. 44(1), pages 43-73.
  10. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
  11. Iain Fraser, 2000. "An application of maximum entropy estimation: the demand for meat in the United Kingdom," Applied Economics, Taylor & Francis Journals, vol. 32(1), pages 45-59.
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