Testing for spatial autocorrelation: the regressors that make the power disappear
AbstractWe 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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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 10542.
Date of creation: Sep 2008
Date of revision:
Cliff-Ord test; point optimal tests; power; spatial error model; spatial lag model; spatial unit root;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-09-20 (All new papers)
- NEP-ECM-2008-09-20 (Econometrics)
- NEP-GEO-2008-09-20 (Economic Geography)
- NEP-ORE-2008-09-20 (Operations Research)
- NEP-URE-2008-09-20 (Urban & Real Estate Economics)
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