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Testing for spatial autocorrelation: the regressors that make the power disappear

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Author Info
Martellosio, Federico

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Abstract

We show that for any sample size, any size of the test, and any weights matrix outside a small class of exceptions, there exists a positive measure set of regression spaces such that the power of the Cliff-Ord test vanishes as the autocorrelation increases in a spatial error model. This result extends to the tests that define the Gaussian power envelope of all invariant tests for residual spatial autocorrelation. In most cases, the regression spaces such that the problem occurs depend on the size of the test, but there also exist regression spaces such that the power vanishes regardless of the size. A characterization of such particularly hostile regression spaces is provided.

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Publisher Info
Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 10542.

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Date of creation: Sep 2008
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Handle: RePEc:pra:mprapa:10542

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Related research
Keywords: Cliff-Ord test point optimal tests power spatial error model spatial lag model spatial unit root.

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Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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  1. Martellosio, Federico, 2006. "Power Properties of Invariant Tests for Spatial Autocorrelation in Linear Regression," MPRA Paper 7255, University Library of Munich, Germany, revised Aug 2008. [Downloadable!]
  2. Harry H. Kelejian & Ingmar R. Prucha & Yevgeny Yuzefovich, 2006. "Estimation Problems In Models With Spatial Weighting Matrices Which Have Blocks Of Equal Elements," Journal of Regional Science, Blackwell Publishing, vol. 46(3), pages 507-515. [Downloadable!] (restricted)
  3. Badi H. Baltagi, 2006. "Random effects and Spatial Autocorrelations with Equal Weights," Center for Policy Research Working Papers 89, Center for Policy Research, Maxwell School, Syracuse University. [Downloadable!]
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  4. Case, Anne C, 1991. "Spatial Patterns in Household Demand," Econometrica, Econometric Society, vol. 59(4), pages 953-65, July. [Downloadable!] (restricted)
  5. Olivier Parent & James P. LeSage, 2008. "Using the variance structure of the conditional autoregressive spatial specification to model knowledge spillovers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(2), pages 235-256. [Downloadable!]
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  6. Kelejian, Harry H. & Prucha, Ingmar R., 2002. "2SLS and OLS in a spatial autoregressive model with equal spatial weights," Regional Science and Urban Economics, Elsevier, vol. 32(6), pages 691-707, November. [Downloadable!] (restricted)
  7. 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. [Downloadable!] (restricted)
  8. H. Kelejian, Harry & Prucha, Ingmar R., 2001. "On the asymptotic distribution of the Moran I test statistic with applications," Journal of Econometrics, Elsevier, vol. 104(2), pages 219-257, September. [Downloadable!] (restricted)
  9. De Long, J Bradford & Summers, Lawrence H, 1991. "Equipment Investment and Economic Growth," The Quarterly Journal of Economics, MIT Press, vol. 106(2), pages 445-502, May. [Downloadable!] (restricted)
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  10. A. F. Militino & M. D. Ugarte & L. García-Reinaldos, 2004. "Alternative Models for Describing Spatial Dependence among Dwelling Selling Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 193-209, 09. [Downloadable!]
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