Refinements in maximum likelihood inference on spatial autocorrelation in panel data
Refinements in maximum likelihood inference on spatial autocorrelation in panel data
In a panel data model with fixed effects, possible cross-sectional dependence is investigated in a spatial autoregressive setting. An Edgeworth expansion is developed for the maximum likelihood estimate of the spatial correlation coefficient. The expansion is used to develop more accurate interval estimates for the coefficient, and tests for cross-sectional independence that have better size properties, than corresponding rules of statistical inference based on first order asymptotic theory. Comparisons of finite sample performance are carried out using Monte Carlo simulations.
447-456
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)
Refinements in maximum likelihood inference on spatial autocorrelation in panel data.
Journal of Econometrics, 189 (2), .
(doi:10.1016/j.jeconom.2015.03.036).
Abstract
In a panel data model with fixed effects, possible cross-sectional dependence is investigated in a spatial autoregressive setting. An Edgeworth expansion is developed for the maximum likelihood estimate of the spatial correlation coefficient. The expansion is used to develop more accurate interval estimates for the coefficient, and tests for cross-sectional independence that have better size properties, than corresponding rules of statistical inference based on first order asymptotic theory. Comparisons of finite sample performance are carried out using Monte Carlo simulations.
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e-pub ahead of print date: 20 March 2015
Published date: December 2015
Organisations:
Economics
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Local EPrints ID: 394839
URI: http://eprints.soton.ac.uk/id/eprint/394839
ISSN: 0304-4076
PURE UUID: 2bce6d55-2b49-4e40-90cc-5dd354c633a5
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Date deposited: 24 May 2016 14:36
Last modified: 15 Mar 2024 05:35
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Author:
Peter M. Robinson
Author:
Francesca Rossi
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