Abstract
The software for spatial econometrics available in the R system for statistical computing is reviewed. The methods are illustrated in a historical perspective, highlighting the main lines of development and employing historically relevant datasets in the examples. Estimators and tests for spatial cross-sectional and panel models based either on maximum likelihood or on generalized moments methods are presented. The paper is concluded reviewing some current active lines of research in spatial econometric software methods.
Highlights
IntroductionSessions of the North American Regional Science Association annual meetings were populated by research analyzing regional data from a spatial econometric perspective
This happened with the emergence of the dedicated R package splm described here; and, some years later, with the Stata add-on package ’xsmle’ [63]
This paper was dedicated to a review of the functionality for spatial econometric methods available in the R system for statistical computing, in the light of the historical developments of methods, mostly following a chronological order and hinting when appropriate at implementations in different software environments
Summary
Sessions of the North American Regional Science Association annual meetings were populated by research analyzing regional data from a spatial econometric perspective. This was probably due to certain similarities between spatial econometrics and methods typically adopted in regional science as for example Input-Output analysis. A multiplicity of methods and models have been developed for cross-sectional as well as panel data [2,3,4]. The aim of this paper is to survey all the available spatial econometrics packages and methods in R that deal with polygon spatial data, presenting the interested researchers with an up-to-date and comprehensive review of methods in both the cross-sectional and the panel data domains.
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