A Command for Estimating Spatial-Autoregressive Models with Spatial-Autoregressive Disturbances and Additional Endogenous Variables

128 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Spatial Panel-data Models Using Stata (2017, unknown): “The methods implemented in this command are the panel data extension of those available in Kelejian and Prucha (2007) and Drukker et al. (2013c).”
Creating and Managing Spatial-Weighting Matrices with the Spmat Command (2013, methodology): “Putting the eigenvalues into the spmat object can dramatically speed up the computations performed by the spreg ml command; see Drukker, Prucha, and Raciborski (2013b) for details and references therein.”
Comparing Implementations of Estimation Methods for Spatial Econometrics (2015, methodology): “The reason is that in their original contribution Kelejian and Prucha (1999) Moving now to the three implementations that are based on Drukker et al. (2013) moment conditions, we observe that, while Stata and spreg (available from R ) present exactly the same results, some distinctions are observed…”
Maximum Likelihood and Generalized Spatial Two-Stage Least-Squares Estimators for a Spatial-Autoregressive Model with Spatial-Autoregressive Disturbances (2013, methodology): “…regression model with exogenous variables. spreg requires that the spatial-weighting matrices M and W be provided in the form of an spmat object as described in Drukker et al. (2013). spreg gs2sls supports both general and banded spatial-weighting matrices; spreg ml supports general matrices only.”