Stata implementation of the non-parametric spatial heteroskedasticity and autocorrelation consistent estimator
AbstractThis talk introduces two Stata routines to implement the non-parametric heteroskedasticity and autocorrelation consistent (SHAC) estimator of the varianceâ€“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 InfoPaper provided by Stata Users Group in its series SAN12 Stata Conference with number 24.
Date of creation: 01 Aug 2012
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-08-23 (All new papers)
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- 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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