Improved tests for spatial correlation

Peter M. Robinson and Francesca Rossi ()

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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 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.

Keywords: spatial autocorrelation; ordinary least squares; hypothesis testing; edgeworth expansion; bootstrap (search for similar items in EconPapers)
JEL-codes: J1 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2013-05-22
New Economics Papers: this item is included in nep-geo
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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http://eprints.lse.ac.uk/58092/ Open access version. (application/pdf)

Related works:
Working Paper: Improved Tests for Spatial Correlation (2013)
Working Paper: Improved tests for spatial correlation (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:58092

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