The Estimation of Spatial Autoregressive Models with Missing data of the Dependent Variables

This paper focuses on several estimation methods for SAR- models in case of missing observations in the dependent variable. First, we show with an example and then in general, how missing observations can change the model and thus resulting in the failure of the 'traditional' estimation methods. To estimate the SAR- model with missings we propose different estimation methods, like GMM, NLS and OLS. We will suggest to derive some of the estimators based on a model approximation. A Monte Carlo Simulation is conducted to compare the different estimation methods in their diverse numerical and sample size aspects.