Robinson, Peter M. and Rossi, Francesca (2012): Improved tests for spatial correlation.

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Abstract
We consider testing the null hypothesis of no spatial autocorrelation against the alternative of first order spatial autoregression. A Wald test statistic has good first order asymptotic properties, but these may not be relevant in small or moderatesized samples, especially as (depending on properties of the spatial weight matrix) the usual parametric rate of convergence may not be attained. We thus develop tests with more accurate size properties, by means of Edgeworth expansions and the bootstrap. The finitesample performance of the tests is examined in Monte Carlo simulations.
Item Type:  MPRA Paper 

Original Title:  Improved tests for spatial correlation 
Language:  English 
Keywords:  Spatial Autocorrelation; Ordinary Least Squares; Hypothesis Testing; Edgeworth Expansion; Bootstrap 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C12  Hypothesis Testing: General C  Mathematical and Quantitative Methods > C2  Single Equation Models; Single Variables > C21  CrossSectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions 
Item ID:  41835 
Depositing User:  Francesca Rossi 
Date Deposited:  09. Oct 2012 20:08 
Last Modified:  14. Feb 2013 13:18 
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URI:  http://mpra.ub.unimuenchen.de/id/eprint/41835 