Article ID Journal Published Year Pages File Type
5097388 Journal of Econometrics 2007 24 Pages PDF
Abstract
We suggest a non-parametric heteroscedasticity and autocorrelation consistent (HAC) estimator of the variance-covariance (VC) matrix for a vector of sample moments within a spatial context. We demonstrate consistency under a set of assumptions that should be satisfied by a wide class of spatial models. We allow for more than one measure of distance, each of which may be measured with error. Monte Carlo results suggest that our estimator is reasonable in finite samples. We then consider a spatial model containing various complexities and demonstrate that our HAC estimator can be applied in the context of that model.
Related Topics
Physical Sciences and Engineering Mathematics Statistics and Probability
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