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Abstract

This paper derives some exact power properties of tests for spatial autocorrelation in thecontext of a linear regression model. In particular, we characterize the circumstances in which thepower vanishes as the autocorrelation increases, thus extending the work of Krämer 2005, Journal ofStatistical Planning and Inference 128, 489-496. More generally, the analysis in the paper sheds newlight on how the power of tests for spatial autocorrelation is affected by the matrix of regressors andby the spatial structure. We mainly focus on the problem of residual spatial autocorrelation, in whichcase it is appropriate to restrict attention to the class of invariant tests, but we also consider the casewhen the autocorrelation is due to the presence of a spatially lagged dependent variable among theregressors. A numerical study aimed at assessing the practical relevance of the theoretical results isincluded.



Item Type: MPRA Paper -

Original Title: Power Properties of Invariant Tests for Spatial Autocorrelation in Linear Regression-

Language: English-

Keywords: Cliff-Ord test; invariant tests; linear regression model; point optimal tests; power; similar tests; spatial autocorrelation-

Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: GeneralC - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C31 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions ; Social Interaction ModelsC - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C21 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile RegressionsC - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics-





Author: Martellosio, Federico

Source: https://mpra.ub.uni-muenchen.de/7255/



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