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Comparing Implementations of Estimation Methods for Spatial Econometrics

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  • Roger Bivand

    (Norwegian School of Econonomics)

  • Gianfranco Piras

    (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.

Suggested Citation

  • Roger Bivand & Gianfranco Piras, 2013. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Working Papers Working Paper 2013-01, Regional Research Institute, West Virginia University.
  • Handle: RePEc:rri:wpaper:2013wp01
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    File URL: https://researchrepository.wvu.edu/rri_pubs/9/
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    More about this item

    Keywords

    spatial econometrics; maximum likelihood; generalized method of moments; estimation; R; Stata; Python; MATLAB;
    All these keywords.

    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

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