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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.

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Bibliographic Info

Paper provided by Stata Users Group in its series SAN12 Stata Conference with number 24.

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Date of creation: 01 Aug 2012
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Handle: RePEc:boc:scon12:24

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Web page: http://stata.com/meeting/sandiego12/
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  1. 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.
  2. 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, 03.
  3. 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, 08.
  4. 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.
  5. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, 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. Mittelhammer,Ron C. & Judge,George G. & Miller,Douglas J., 2000. "Econometric Foundations Pack with CD-ROM," Cambridge Books, Cambridge University Press, number 9780521623940, October.
  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.
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