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Stata implementation of the non-parametric spatial heteroskedasticity and autocorrelation consistent estimator

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  • P. Wilner Jeanty

    () (Hobby Center for the Study of Texas/Kinder Institute for Urban Research, Rice University)

Abstract

This talk introduces two Stata routines to implement the non-parametric heteroskedasticity and autocorrelation consistent (SHAC) estimator of the variance–covariance matrix in a spatial context, as proposed by Conley (1999) and Kelejian and Prucha (2007). The (SHAC) estimator is robust against potential misspecification of the disturbance terms and allows for unknown forms of heteroskedasticity and correlation across spatial units. Heteroskedasticity is likely to arise when spatial units differ in size or structural features.

Suggested Citation

  • P. Wilner Jeanty, 2012. "Stata implementation of the non-parametric spatial heteroskedasticity and autocorrelation consistent estimator," SAN12 Stata Conference 24, Stata Users Group.
  • Handle: RePEc:boc:scon12:24
    as

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    File URL: http://fmwww.bc.edu/repec/san2012/jeanty.san2012.pdf
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    References listed on IDEAS

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    1. Marlon G. Boarnet & Saksith Chalermpong & Elizabeth Geho, 2005. "Specification issues in models of population and employment growth," Papers in Regional Science, Wiley Blackwell, vol. 84(1), pages 21-46, March.
    2. 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.
    3. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
    4. Bernard Fingleton & Julie Le Gallo, 2008. "Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances: Finite sample properties," Papers in Regional Science, Wiley Blackwell, vol. 87(3), pages 319-339, August.
    5. 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.
    6. Cameron,A. Colin & Trivedi,Pravin K., 2008. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9787111235767, May.
    7. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, September.
    8. Jeanty, P. Wilner & Partridge, Mark & Irwin, Elena, 2010. "Estimation of a spatial simultaneous equation model of population migration and housing price dynamics," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 343-352, September.
    9. 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.
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    Cited by:

    1. Michael D. König & Dominic Rohner & Mathias Thoenig & Fabrizio Zilibotti, 2016. "The Long-lasting Shadow of the Allied Occupation of Austria on its Spatial Equilibrium," HiCN Working Papers 231, Households in Conflict Network.
    2. Daniel Oto-Peralías & Diego Romero-Ávila, 2016. "The economic consequences of the Spanish Reconquest: the long-term effects of Medieval conquest and colonization," Journal of Economic Growth, Springer, vol. 21(4), pages 409-464, December.

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