Comparing Implementations of Estimation Methods for Spatial Econometrics
AbstractRecent 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.
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Bibliographic InfoPaper provided by Regional Research Institute, West Virginia University in its series Working Papers with number 201301.
Length: 38 pages
Date of creation: Jan 2013
Date of revision:
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More information through EDIRC
spatial econometrics; maximum likelihood; generalized method of moments; estimation; R; Stata; Python; MATLAB;
Find related papers by JEL classification:
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-08-20 (All new papers)
- NEP-ECM-2014-08-20 (Econometrics)
- NEP-GEO-2014-08-20 (Economic Geography)
- NEP-URE-2014-08-20 (Urban & Real Estate Economics)
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