Improved tests for spatial correlation
AbstractWe 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 moderate-sized 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 finite-sample performance of the tests is examined in Monte Carlo simulations.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 41835.
Date of creation: 22 Jun 2012
Date of revision:
Spatial Autocorrelation; Ordinary Least Squares; Hypothesis Testing; Edgeworth Expansion; Bootstrap;
Other versions of this item:
- 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-2012-10-20 (All new papers)
- NEP-ECM-2012-10-20 (Econometrics)
- NEP-GEO-2012-10-20 (Economic Geography)
- NEP-URE-2012-10-20 (Urban & Real Estate Economics)
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