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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)

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    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.

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    File URL: http://rri.wvu.edu/wp-content/uploads/2012/11/Piras_BivandWP2013.pdf
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    Bibliographic Info

    Paper provided by Regional Research Institute, West Virginia University in its series Working Papers with number 201301.

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    Length: 38 pages
    Date of creation: Jan 2013
    Date of revision:
    Handle: RePEc:rri:wpaper:201301

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    Keywords: spatial econometrics; maximum likelihood; generalized method of moments; estimation; R; Stata; Python; MATLAB;

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    References

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    1. Kelejian, Harry H. & Piras, Gianfranco, 2011. "An extension of Kelejian's J-test for non-nested spatial models," Regional Science and Urban Economics, Elsevier, vol. 41(3), pages 281-292, May.
    2. Giovanni Millo & Gianfranco Piras, . "splm: Spatial Panel Data Models in R," Journal of Statistical Software, American Statistical Association, vol. 47(i01).
    3. John C. Nash & Ravi Varadhan, . "Unifying Optimization Algorithms to Aid Software System Users: optimx for R," Journal of Statistical Software, American Statistical Association, vol. 43(i09).
    4. Florax, R. & Folmer, H., 1991. "Specification and Estimation of Spatial Linear Regression Models: Monte Carlo Evaluation of Pre-Test Estimator," Mansholt Working Papers 1991-4, Wageningen University, Mansholt Graduate School of Social Sciences.
    5. Florax, Raymond J. G. M. & Folmer, Hendrik & Rey, Sergio J., 2003. "Specification searches in spatial econometrics: the relevance of Hendry's methodology," Regional Science and Urban Economics, Elsevier, vol. 33(5), pages 557-579, September.
    6. David M. Drukker & Ingmar Prucha & Rafal Raciborski, 2013. "A command for estimating spatial-autoregressive models with spatial-autoregressive disturbances and additional endogenous variables," Stata Journal, StataCorp LP, vol. 13(2), pages 287-301, June.
    7. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    8. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
    9. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, 03.
    10. Gianfranco Piras, . "sphet: Spatial Models with Heteroskedastic Innovations in R," Journal of Statistical Software, American Statistical Association, vol. 35(i01).
    11. Lee, Lung-fei, 2007. "GMM and 2SLS estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 137(2), pages 489-514, April.
    12. Irani Arraiz & David M. Drukker & Harry H. Kelejian & Ingmar R. Prucha, 2010. "A Spatial Cliff-Ord-Type Model With Heteroskedastic Innovations: Small And Large Sample Results," Journal of Regional Science, Wiley Blackwell, vol. 50(2), pages 592-614.
    13. Rey, S.J., 2008. "Show me the code: Spatial analysis and open source," MPRA Paper 9260, University Library of Munich, Germany.
    14. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
    15. Luc Anselin & Nancy Lozano-Gracia, 2008. "Errors in variables and spatial effects in hedonic house price models of ambient air quality," Empirical Economics, Springer, vol. 34(1), pages 5-34, February.
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    Cited by:
    1. Feichtinger, Paul & Salhofer, Klaus, 2013. "A Spatial Analysis of Agricultural Land Prices in Bavaria," Working Papers 160741, Factor Markets, Centre for European Policy Studies.
    2. Feichtinger, Paul & Salhofer, Paul, 2013. "A Spatial Analysis of Agricultural Land Prices in Bavaria," Factor Markets Working Papers 162, Centre for European Policy Studies.

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