Improved Tests for Spatial Correlation

Peter M Robinson and Francesca Rossi ()

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

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: C12 C21 (search for similar items in EconPapers)
Date: 2013-05
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://sticerd.lse.ac.uk/dps/em/em565.pdf (application/pdf)

Related works:
Working Paper: Improved tests for spatial correlation (2013)
Working Paper: Improved tests for spatial correlation (2012)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:565

Access Statistics for this paper

More papers in STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE
Bibliographic data for series maintained by ().