Impact measures in spatial autoregressive models

Researchers often make use of linear regression models in order to assess the impact of policies on target outcomes. In a correctly specified linear regression model, the marginal impact is simply measured by the linear regression coefficient. However, when dealing with both synchronic and diachronic spatial data, the interpretation of the parameters is more complex because the effects of policies extend to the neighboring locations. Summary measures have been suggested in the literature for the cross-sectional spatial linear regression models and spatial panel datamodels. Inthis article,wecompare threeprocedures fortestingthesignificance ofimpactmeasuresinthespatiallinearregressionmodels.Theseproceduresinclude (i) the estimating equation approach, (ii) the classical delta method, and (iii) the simulationmethod.InaMonteCarlostudy,wecomparethefinitesampleproperties of these procedures.