Updating weighting matrices by Cross-Entropy
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.
Volume (Year): (2011)
Issue (Month): 21 ()
|Contact details of provider:|| Postal: C/ Viladomat, 321 entresuelo - 08029 Barcelona, Teléfono y Fax: + 34 933101112, E-mail: email@example.com, Web: www.aecr.org, Web Investigaciones Regionales: www.investigacionesregionales.org|
Phone: +34 93 310 11 12
Fax: +34 93 310 11 12
Web page: http://www.aecr.org/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- 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.
- 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.
- Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," Review of Economic Studies, Oxford University Press, vol. 60(3), pages 531-542.
- 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.
- 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.
- Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
- Pedro Cantos & Mercedes Gumbau‐Albert & Joaquín Maudos, 2003. "Transport infrastructures, spillover effects and regional growth: evidence of the Spanish case," Transport Reviews, Taylor & Francis Journals, vol. 25(1), pages 25-50, December.
- 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.
- Esteban FernÃ¡ndez-VÃ¡zquez & MatÃas Mayor-FernÃ¡ndez & Jorge RodrÃguez-VÃ¡lez, 2009. "Estimating Spatial Autoregressive Models by GME-GCE Techniques," International Regional Science Review, , vol. 32(2), pages 148-172, April.
When requesting a correction, please mention this item's handle: RePEc:ris:invreg:0031. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Julieta Llungo-Ortíz)
If references are entirely missing, you can add them using this form.