Identifying Latent Group Structures in Spatial Panel Models with Common Shocks

Music City Center 

This paper considers the joint estimation and identification of latent group structures in dynamic spatial panel data models with common shocks. We consider a spatial panel data model that allows for both weak and strong cross-sectional correlations, where weak correlations are captured by a spatial structure and strong correlations are captured by a factor structure. The latent group structures allow individuals to be classified into different groups where the number of groups and the group memberships are unknown. The individuals within a group have common slope parameters, while parameter heterogeneity is allowed across the groups. To estimate the number of groups and identify the latent group structures, a pairwise fusion penalized quasi-maximum likelihood approach is proposed. We provide the asymptotic analysis of the proposed approach. The asymptotic analysis demonstrate the desirable properties of the method, including classification consistency, and the oracle property of the post-classification estimator. The proposed method is further illustrated by simulation studies which demonstrate the good finite sample performance of the method, and is applied to the real house price data across 377 Metropolitan Statistical Areas in the US which suggest the presence of group structures.

Classification; Common shocks; Latent group structures; Pairwise adaptive; group fused Lasso; Panel data models; Spatial interactions