Abstract

In recent decades, there is growing literature on the estimation of dynamic panel data models (see Phillips and Moon 1999; Hahn and Kuersteiner 2002; Alvarez and Arellano 2003; Hahn and Newey 2004, etc.). For the panel data with spatial interactions, Kapoor, Kelejian, and Prucha (2007) extend the asymptotic analysis of the method of moments estimators to a spatial panel model with error components, where T is finite. Baltagi, Song, Jung, and Koh (2007) consider the testing of spatial and serial dependence in an extended model, where serial correlation on each spatial unit over time and spatial dependence across spatial units are allowed in the disturbances. Su and Yang (2007) study the dynamic panel data with spatial error and random effects. These panel models specify the spatial correlation by including spatially correlated disturbances but do not incorporate a spatial autoregressive term in the regression equation. With large n and moderate or large T, Korniotis (2005) studies a time-space recursive model where only an individual time lag and a spatial time lag are present but not a contemporaneous spatial lag. A general model could be the spatial dynamic panel data (SDPD) where a contemporaneous spatial lag is also included. Yu, de Jong, and Lee (2007, 2008) and Yu and Lee (2010) study, respectively, the spatial cointegration, stable, and unit root SDPD models, where the individual time lag, spatial time lag and contemporaneous spatial lag are all included.