Refined tests for spatial correlation
Refined tests for spatial correlation
We consider testing the null hypothesis of no spatial correlation against the alternative of pure first order spatial autoregression. A test statistic based on the least squares estimate 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. Although the least squares estimate is inconsistent for the correlation parameter, we show that under quite general conditions its probability limit has the correct sign, and that least squares testing is consistent; we also establish asymptotic local power properties. The finite-sample performance of our tests is compared with others in Monte Carlo simulations
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Robinson, Peter M.
edc1b0dd-75cb-47f4-8839-f21e5904b23a
Rossi, Francesca
1cdd87b3-bc01-40b0-ad91-0db0ee24e8e0
December 2015
Robinson, Peter M.
edc1b0dd-75cb-47f4-8839-f21e5904b23a
Rossi, Francesca
1cdd87b3-bc01-40b0-ad91-0db0ee24e8e0
Robinson, Peter M. and Rossi, Francesca
(2015)
Refined tests for spatial correlation.
Econometric Theory, 31 (4), .
(doi:10.1017/S0266466614000498).
Abstract
We consider testing the null hypothesis of no spatial correlation against the alternative of pure first order spatial autoregression. A test statistic based on the least squares estimate 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. Although the least squares estimate is inconsistent for the correlation parameter, we show that under quite general conditions its probability limit has the correct sign, and that least squares testing is consistent; we also establish asymptotic local power properties. The finite-sample performance of our tests is compared with others in Monte Carlo simulations
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e-pub ahead of print date: 4 November 2014
Published date: December 2015
Organisations:
Economics
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Local EPrints ID: 370010
URI: http://eprints.soton.ac.uk/id/eprint/370010
PURE UUID: cb836220-11b2-4b56-95f1-5cd8a645ea08
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Date deposited: 20 Oct 2014 10:49
Last modified: 14 Mar 2024 18:11
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Author:
Peter M. Robinson
Author:
Francesca Rossi
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