A Consistent Nonparametric Estimation of Spatial Autocovariances

Theophile Azomahou and Dong Li
Additional contact information
Theophile Azomahou: BETA - Bureau d'Économie Théorique et Appliquée - INRA - Institut National de la Recherche Agronomique - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique

Post-Print from HAL

Abstract: We examine some aspects of estimating sample autocovariances for spatial processes. Especially, we note that for such processes, it is not possible to approximate the expectation by the sample mean, like in the case of time series data. Then, we propose a consistent nonparametric estimation of sample autocovariances for an irregularly scattered spatial process, derived from a transformation of the initial process. We also suggest an L_2-consistent weighting matrix. Monte Carlo simulations are used to evaluate the performance of the proposed estimators in finite samples.

Keywords: Spatial Autocovariances; Monte Carlo simulations (search for similar items in EconPapers)
Date: 2008
References: Add references at CitEc
Citations:

Published in Economics Bulletin, 2008, 3 (29), pp.1-10

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: A consistent nonparametric estimation of spatial autocovariances (2005)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00279181

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().