HAC Estimation in a Spatial Framework
Harry H. Kelejian and Ingmar Prucha ,
1
( 140 )
Journal of Econometrics
131-154
September
2007
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.

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