Spatial heteroskedasticity and autocorrelation consistent estimation of covariance matrix
This paper considers spatial heteroskedasticity and autocorrelation consistent (spatial HAC) estimation of covariance matrices of parameter estimators. We generalize the spatial HAC estimator introduced by Kelejian and Prucha (2007) to apply to linear and nonlinear spatial models with moment conditions. We establish its consistency, rate of convergence and asymptotic truncated mean squared error (MSE). Based on the asymptotic truncated MSE criterion, we derive the optimal bandwidth parameter and suggest its data dependent estimation procedure using a parametric plug-in method. The finite sample performances of the spatial HAC estimator are evaluated via Monte Carlo simulation.
Year of publication: |
2011
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---|---|
Authors: | Kim, Min Seong ; Sun, Yixiao |
Published in: |
Journal of Econometrics. - Elsevier, ISSN 0304-4076. - Vol. 160.2011, 2, p. 349-371
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Publisher: |
Elsevier |
Keywords: | Asymptotic mean squared error Heteroskedasticity and autocorrelation Covariance matrix estimator Optimal bandwidth choice Robust standard error Spatial dependence |
Saved in:
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