DYNAMIC PANEL DATA MODELS WITH SPATIALLY CORRELATED DISTURBANCES

This thesis considers a dynamic panel data model with error components that are correlated both spatially (cross-sectionally) and time-wise. The model extends the literature on dynamic panel data models with cross-sectionally independent error components. The model for spatial dependence is a Cliff-Ord type model. We introduce a three step estimation procedure and give formal large sample results for the case of a finite time dimension. In particular, we show that a simple first stage instrumental variable (IV) estimator, that ignores the spatial correlation of the errors, is consistent and √N-consistent, where N denotes the cross-sectional dimension. We then extend the generalized moments estimator introduced by Kelejian and Prucha (1999) for estimating the spatial autoregressive parameter and show that if it is based on a √N-consistently estimated disturbances, it will also be consistent. Finally, we derive a large sample distribution of a second stage generalized method of moments (GMM) estimator based on a consistent estimator of the spatial autoregressive parameter. We also present results from a small Monte Carlo study to illustrate the small sample performance of the proposed estimation procedure.