Comparing Implementations of Estimation Methods for Spatial Econometrics

Working Papers from Regional Research Institute, West Virginia University

Abstract: Recent advances in the implementation of spatial econometrics model estimation techniques have made it desirable to compare results, which should correspond between implementations across software applications for the same data. These model estimation techniques are associated with methods for estimating impacts (emanating effects), which are also presented and compared. This review constitutes an up to date comparison of generalized method of moments (GMM) and maximum likelihood (ML) implementations now available. The comparison uses the cross sectional US county data set provided by Drukker, Prucha, and Raciborski (2011c, pp. 6-7). The comparisons will be cast in the context of alternatives using the MATLAB Spatial Econometrics toolbox, Stata, Python with PySAL (GMM) and R packages including sped, sphet and McSpatial.

Keywords: spatial econometrics; maximum likelihood; generalized method of moments; estimation; R; Stata; Python; MATLAB (search for similar items in EconPapers)
JEL-codes: C21 C4 C5 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2013-01
New Economics Papers: this item is included in nep-ecm and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Related works:
Journal Article: Comparing Implementations of Estimation Methods for Spatial Econometrics (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:rri:wpaper:2013wp01

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