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Updating weighting matrices by Cross-Entropy

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Author Info

  • Fernández Vázquez, Esteban

    ()
    (Department of Applied Economics-University of Oviedo)

Abstract

The classical approach to estimate spatial models lays on the choiceof a spatial weights matrix that reflects the interactions among locations. The ruleused to define this matrix is supposed to be the most similar to the «true» spatialrelationships, but for the researcher is difficult to elucidate when the choice of thismatrix is right and when is wrong. This key step in the process of estimating spatialmodels is a somewhat arbitrary choice, as Anselin (2002) pointed out, and itcan be seen as one of their main methodological problems. This note proposes notimposing the elements of the spatial matrix but estimating them by cross entropy(CE) econometrics. Since the spatial weight matrices are often row-standardized,each one of their rows can be approached as probability distributions. EntropyEconometrics (EE) techniques are a useful tool for recovering unknown probabilitydistributions and its application allows the estimation of the elements of thespatial weights matrix instead of the imposition by researcher. Hence, the spatiallag matrix is not a matter of choice for researcher but of empirical estimation byCE. We compare classical with CE estimators by means of Monte Carlo simulationsin several scenarios on the true spatial effect. The results show that CrossEntropy estimates outperform the classical estimates, especially when the specificationof the weights matrix is not similar to the true one. This result points to CEas a helpful technique to reduce the degree of arbitrariness imposed in the estimationof spatial models.

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Bibliographic Info

Article provided by Asociación Española de Ciencia Regional in its journal Investigaciones Regionales.

Volume (Year): (2011)
Issue (Month): 21 ()
Pages: 53-69

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Handle: RePEc:ris:invreg:0031

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Related research

Keywords: Spatial econometrics; cross entropy econometrics; spatial models specifications; Monte Carlo simulations;

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References

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  1. Case, Anne C. & Rosen, Harvey S. & Hines, James Jr., 1993. "Budget spillovers and fiscal policy interdependence : Evidence from the states," Journal of Public Economics, Elsevier, vol. 52(3), pages 285-307, October.
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  3. repec:att:wimass:9127 is not listed on IDEAS
  4. Douglas Holtz-Eakin & Amy Ellen Schwartz, 1995. "Spatial Productivity Spillovers from Public Infrastructure: Evidence from State Highways," NBER Working Papers 5004, National Bureau of Economic Research, Inc.
  5. Manski, Charles F, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," Review of Economic Studies, Wiley Blackwell, vol. 60(3), pages 531-42, July.
  6. 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.
  7. 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.
  8. Douglas Holtz-Eakin & Amy Schwartz, 1995. "Spatial productivity spillovers from public infrastructure: Evidence from state highways," International Tax and Public Finance, Springer, vol. 2(3), pages 459-468, October.
  9. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
  10. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
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