Neighbourhood GMM estimation of dynamic panel data models

A new approach is developed for estimation of short dynamic panel data models with spatially correlated errors. The method employs an additional set of moment conditions that become available for each i-specifically, instruments with respect to the individual(s) which unit i is spatially correlated with. These moment conditions are non-redundant and remain informative even if the data generating process is close to a unit root one. The proposed GMM estimator is consistent and asymptotically normally distributed. An extensive Monte Carlo study also builds a GMM estimator that combines spatial and standard instruments. This estimator appears to perform very well under a wide range of parametrisations in terms of both bias and root mean square error. The proposed method is illustrated using crime data based on a panel of 153 local government areas in NSW, spanning a period of 5 years.