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Properties of tests for spatial error components

  • Luc Anselin

    ()

  • Rosina Moreno

    ()

In spatial econometrics, the typical alternative of spatial autocorrelation is expressed in the form of a spatial autorregressive process. While the bulk of the literature is devoted to specification tests and estimation methods for these models, alternatives have been suggested as well. In this paper, we consider alternatives that take the form of the spatial error components formulation proposed by Kelejian and Robinson. We consider a number of specification tests against this alternative, based on both a maximum likelihood framework as well as on a general method of moments estimation approach. We compare the performance of these tests in a series of Monte Carlo simulation experiments against a wide range of alternatives of spatial autocorrelation, under a number of different error distributions.

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File URL: http://www-sre.wu-wien.ac.at/ersa/ersaconfs/ersa01/papers/full/183.pdf
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Paper provided by European Regional Science Association in its series ERSA conference papers with number ersa01p183.

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Date of creation: Aug 2001
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Handle: RePEc:wiw:wiwrsa:ersa01p183
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  1. Kelejian, Harry H. & Robinson, Dennis P., 1998. "A suggested test for spatial autocorrelation and/or heteroskedasticity and corresponding Monte Carlo results," Regional Science and Urban Economics, Elsevier, vol. 28(4), pages 389-417, July.
  2. 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.
  3. 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.
  4. Russell Davidson & James G. MacKinnon, 1994. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," Working Papers 903, Queen's University, Department of Economics.
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