When spatial data are repeatedly collected from the same spatial locations over a short period of time, a spatial panel/longitudinal data set is generated. Thus, this type of spatial longitudinal data must exhibit both spatial and longitudinal correlations, which are not easy to model. This work is motivated by existing studies in statistics and econometrics literature but the proposed model and inference procedures should be applicable to the spatial panel data encountered in other fields as well such as environmental and/or ecological setups. Specifically, unlike the existing studies, we propose a new dynamic mixed model to accommodate both spatial and panel correlations. A complete theoretical analysis is given for the estimation of regression effects, and spatial and panel correlations by exploiting second and higher order moments based quasi-likelihood methods. Asymptotic properties are also studied in details. The step by step estimation results developed in the paper should be useful to the practitioners dealing with spatial panel data.

Additional Metadata
Keywords 62H12, auto-regression type dynamic model for panel data, consistency and asymptotic normality, Primary 62H11, quasi-likelihood and moment estimation, Secondary 62H20, spatial correlations, Spatial panel dynamic mixed model
Persistent URL dx.doi.org/10.1007/s13171-019-00178-z
Journal Sankhya A
Citation
Sutradhar, B.C. (2019). An Overview on Econometric Models for Linear Spatial Panel Data. Sankhya A. doi:10.1007/s13171-019-00178-z