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