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Properties of tests for spatial error components

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  • Luc Anselin

    ()

  • Rosina Moreno

    ()

Abstract

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.

Suggested Citation

  • Luc Anselin & Rosina Moreno, 2001. "Properties of tests for spatial error components," ERSA conference papers ersa01p183, European Regional Science Association.
    • Handle: RePEc:wiw:wiwrsa:ersa01p183
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    1. 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 509-533, May.
    2. Davidson, Russell & MacKinnon, James G, 1998. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," The Manchester School of Economic & Social Studies, University of Manchester, vol. 66(1), pages 1-26, January.
    3. 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.
    4. 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.
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