Luc Anselin - Current Working Papers

(papers are removed if and when published, unless working papers are different from published version)

(see also selected publications)

• Anselin, Spatial externalities
• Anselin and Moreno, Properties of tests for spatial error components (revised)
• Anselin, Spatial externalities, spatial multipliers and spatial econometrics
• Messner and Anselin, Spatial analyses of homicide with areal data
• Anselin, Under the hood: issues in the specification and interpretation of spatial regression models
• Anselin, Syabri and Smirnov, Visualizing multivariate spatial correlation with dynamically linked windows
• Anselin, Bongiovanni and Lowenberg-DeBoer, A spatial econometric approach to the economics of site-specific nitrogen management in corn production
• Kim, Phipps and Anselin, Measuring the benefits of air quality improvement: a spatial hedonic approach

Luc Anselin
Spatial externalities
(forthcoming International Regional Science Review)

Luc Anselin and Rosina Moreno
Properties of tests for spatial error components
(forthcoming Regional Science and Urban Economics)

Abstract:
In spatial econometrics, the typical alternative of spatial autocorrelation is expressed in the form of a spatial autorregressive process. While the bulk of the literature is devoted to specification tests and estimation methods for this model, alternatives have been suggested as well. In this paper, we consider an alternative that takes the form of the spatial error components formulation proposed by Kelejian and Robinson (1995). We consider a number of specification tests against this alternative, based on both a maximum likelihood framework as well as on a general method of moments estimation approach. We compare the performance of these tests in a series of Monte Carlo simulation experiments for a range of different spatial layouts and under a number of different error distributions.

Luc Anselin
Spatial externalities, spatial multipliers and spatial econometrics
(forthcoming: International Regional Science Review)

Abstract:
This paper outlines a taxonomy of spatial econometric model specifications that incorporate spatial externalities in various ways. The point of departure is a reduced form in which local or global spillovers are expressed as spatial multipliers. From this, a range of familiar and less familiar specifications is derived for the structural form of a spatial regression.

Luc Anselin
Under the hood: issues in the specification and interpretation of spatial regression models
(forthcoming: Agricultural Economics)

Abstract:
This paper reviews a number of conceptual issues pertaining to the implementation of an explicit "spatial" perspective in applied econometrics. It provides an overview of the motivation for including spatial effects in regression models, both from a theory-driven as well as from a data-driven perspective. Considerable attention is paid to the inferential framework necessary to carry out estimation and testing and the different assumptions, constraints and implications embedded in the various specifications available in the literature. The review combines insights from the traditional spatial econometrics literature as well as from geostatistics, biostatistics and medical image analysis.

Luc Anselin, Ibnu Syabri and Oleg Smirnov
Visualizing multivariate spatial correlation with dynamically linked windows

Abstract:
Several recent efforts have focused on adding exploratory data analysis functionality to geographic information systems (GIS) by integrating established statistical software with a GIS. In this paper, we outline an alternative approach, where the functionality is built from scratch, using a combination of small libraries of dedicated functions, rather than relying on the full scope of existing software suites. The suggested approach is modular and freestanding. Whithin an overall framework of dynamically linked windows, it combines a cartographic representation of data on a map with traditional statistical graphics, such as histograms, box plots, and scatterplots. It extends earlier work on the visualization of spatial correlation to a multivariate setting, introducing a Moran Scatterplot Matrix and Multivariate LISA Maps. The new program (DynESDA2) works on both point and polygon coverages, implements true brushing of maps, as well as the usual linking and brushing between maps and statistical graphs.

Luc Anselin, Rodolfo Bongiovanni and Jess Lowenberg-DeBoer
A spatial econometric approach to the economics of site-specific nitrogen management in corn production

Abstract:
Spatial technologies such as GPS and GIS increasingly form the basis for site-specific management in crop production. This paper assesses the contribution of an explicit spatial econometric methodology in the estimation of crop yield functions that are used to optimize fertilizer application. The specific case study is for Nitrogen (N) application to corn production in Argentina, where the implementation of variable rate technology (VRT) requires methods that use inexpensive information and that focus on the inputs and variability common to Argentine growing areas. The objective of the paper is to assess the economic value of the application of spatial regression analysis to yield monitor data as a means to optimize variable rate fertilizer strategies. The data in the case study are from on-farm trials with a uniform N rate along strips and a randomized complete block design to estimate site-specific crop response functions. Spatial autocorrelation and spatial heterogeneity are taken into account in regression estimation of N response functions by landscape position, in the form of both a spatial autoregressive error structure and groupwise heteroskedasticity. Both uniform rate and VRT returns are computed from a partial budget model. The results suggest that N response differs significantly by landscape position, and that VRA for N may be modestly profitable depending on the VRT fee level. Profitability depends crucially on the model specification used, with all spatial models consistently suggesting profitability, whereas the non-spatial models do not.

Chong Won Kim, Tim T. Phipps and Luc Anselin
Measuring the benefits of air quality improvement: a spatial hedonic approach
(forthcoming: Journal of Environmental Economics and Management)

Abstract:
The primary objective of this paper is to improve the methodology for estimating hedonic price functions when the data are inherently spatial. A spatial-econometric hedonic housing price model is developed and estimated for the Seoul metropolitan area to measure the marginal value of improvements in SO2 and NOX concentrations. Diagnostic testing favored the spatial lag model over the spatial error model. Results showed that SO2 pollution levels had a significant impact on housing prices while NOX pollution did not. The authors attribute this differential impact to the relatively higher levels of SO2 pollution when compared with pollution standards and the relative recency of NOX pollution. Marginal WTP for a 4% improvement in mean SO2 concentrations is about $2,333 or 1.4% of mean housing price.