HAC estimation in a spatial framework

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HAC estimation in a spatial framework (2007)

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by Harry H. Kelejian , Ingmar R. Prucha

Venue:	J. Econom
Citations:	62 - 7 self

BibTeX

@ARTICLE{Kelejian07hacestimation,
    author = {Harry H. Kelejian and Ingmar R. Prucha},
    title = {HAC estimation in a spatial framework},
    journal = {J. Econom},
    year = {2007},
    pages = {131--154}
}

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Abstract

We suggest a nonparametric 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.

Keyphrases

hac estimation spatial framework spatial model finite sample sample moment wide class nonparametric heteroscedasticity various complexity spatial context monte carlo result autocorrelation consistent hac estimator

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