Dirty spatial econometrics

Giuseppe Arbia (), Giuseppe Espa () and Diego Giuliani ()

No 2015/09, DEM Discussion Papers from Department of Economics and Management

Abstract: Spatial data are often contaminated with a series of imperfections that reduce their quality and can dramatically distort the inferential conclusions based on spatial econometric modeling. A ÒcleanÓ ideal situation considered in standard spatial econometrics textbooks is when we fit Cliff-Ord-type models to data where the spatial units constitute the full population, there are no missing data and there is no uncertainty on the spatial observations that are free from measurement and locational errors. Unfortunately in practical cases the reality is often very different and the datasets contain all sorts of imperfections: they are often based on a sample drawn from the whole population, some data are missing and they almost invariably contain both attribute and locational errors. This is a situation of ÒdirtyÓ spatial econometric modelling. Through a series of Monte Carlo experiments, this paper considers the effects on spatial econometric model estimation and hypothesis testing of two specific sources of dirt, namely missing data and locational errors.

Date: 2015
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.unitn.it/files/download/27419/dem2015_09.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 403 Forbidden

Related works:
Journal Article: Dirty spatial econometrics (2016)
Journal Article: Dirty spatial econometrics (2016)
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:trn:utwpem:2015/09

Access Statistics for this paper

More papers in DEM Discussion Papers from Department of Economics and Management Contact information at EDIRC.
Bibliographic data for series maintained by roberto.gabriele@unitn.it ().