Estimation of Panel Model with Spatial Autoregressive Error and Common Factors

Abstract

This study explores the estimation of a panel model that combines multifactor error with spatial correlation. On the basis of common correlated effects pooled (CCEP) estimator (Pesaran in Econometrica 74:967---1012, 2006), the generalized moments (GM) procedure suggested by Kelejian and Prucha (Int Econ Rev 40:509---533, 1999) is employed to estimate the spatial autoregressive parameters. These estimators are then used to define feasible generalized least squares (FGLS) procedures for the regression parameters. Given N and T $$\longrightarrow \infty $$?? (jointly), this study provides formal large sample results on the consistency of the proposed GM procedures, as well as the consistency and asymptotic normality of the proposed feasible generalized least squares (FGLS). It is proved that FGLS is more efficient than CCEP. The small sample properties of the various estimators are investigated by Monte Carlo experiments, which confirmed the theoretical conclusions. Results demonstrate that the popular spatial correlation analysis used in previous empirical literature may be misleading because it neglects common factors.