IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

Spatial Structures and Spatial Spillovers: A GME Approach

  • Matias Mayor Fernandez

    ()

  • Esteban Fernandez Vazquez

    ()

  • Jorge Rodriguez Valez

    ()

Spatial econometrics is a subdiscipline that have gained a huge popularity in the last twenty years, not only in theoretical econometrics but in empirical studies as well. Basically, spatial econometric methods measure spatial interaction and incorporate spatial structure into regression analysis. The specification of a matrix of spatial weights W plays a crucial role in the estimation of spatial models. The elements of this matrix measure the spatial relationships between two geographical locations i and j, and they are specified exogenously to the model. Several alternatives for W have been proposed in the literature, although binary matrices based on contiguity among locations or distance matrices are the most commons choices. One shortcoming of using this type of matrices for the spatial models is the impossibility of estimating “heterogeneous†spatial spillovers: the typical objective is the estimation of a parameter that measures the average spatial effect of the set of locations analysed. Roughly speaking, this is given by “ill-posed†econometric models where the number of (spatial) parameters to estimate is too large. In this paper, we explore the use of generalized maximum entropy econometrics (GME) to estimate spatial structures. This technique is very attractive in situations where one has to deal with estimation of “ill-posed†or “ill-conditioned†models. We compare by means of Monte Carlo simulations “classical†ML estimators with GME estimators in several situations with different availability of information.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www-sre.wu-wien.ac.at/ersa/ersaconfs/ersa06/papers/777.pdf
Download Restriction: no

Paper provided by European Regional Science Association in its series ERSA conference papers with number ersa06p777.

as
in new window

Length:
Date of creation: Aug 2006
Date of revision:
Handle: RePEc:wiw:wiwrsa:ersa06p777
Contact details of provider: Postal: Welthandelsplatz 1, 1020 Vienna, Austria
Web page: http://www.ersa.org

References listed on IDEAS
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.:

as in new window
  1. F Stetzer, 1982. "Specifying weights in spatial forecasting models: the results of some experiments," Environment and Planning A, Pion Ltd, London, vol. 14(5), pages 571-584, May.
  2. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, September.
  3. Bernard Fingleton, 2001. "Equilibrium and Economic Growth: Spatial Econometric Models and Simulations," Journal of Regional Science, Wiley Blackwell, vol. 41(1), pages 117-147.
  4. 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.
  5. 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.
  6. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
  7. Jeffrey P. Cohen & Catherine J. Morrison Paul, 2004. "Public Infrastructure Investment, Interstate Spatial Spillovers, and Manufacturing Costs," The Review of Economics and Statistics, MIT Press, vol. 86(2), pages 551-560, May.
  8. 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.
  9. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
  10. Case, Anne C, 1991. "Spatial Patterns in Household Demand," Econometrica, Econometric Society, vol. 59(4), pages 953-65, July.
  11. 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.
  12. 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.
  13. Cornelis Gardebroek & Alfons G.J.M. Oude Lansink, 2004. "Farm-specific Adjustment Costs in Dutch Pig Farming," Journal of Agricultural Economics, Wiley Blackwell, vol. 55(1), pages 3-24.
  14. Dubin, Robin A., 1998. "Spatial Autocorrelation: A Primer," Journal of Housing Economics, Elsevier, vol. 7(4), pages 304-327, December.
  15. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-33, May.
  16. Quirino Paris & Richard E. Howitt, 1998. "An Analysis of Ill-Posed Production Problems Using Maximum Entropy," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(1), pages 124-138.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:wiw:wiwrsa:ersa06p777. 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: (Gunther Maier)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.