Properties of tests for spatial error components
AbstractIn 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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Bibliographic InfoPaper provided by European Regional Science Association in its series ERSA conference papers with number ersa01p183.
Date of creation: Aug 2001
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- Russell Davidson & James G. MacKinnon, 1994.
"Graphical Methods for Investigating the Size and Power of Hypothesis Tests,"
903, Queen's University, Department of Economics.
- 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.
- 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.
- 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.
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