Random effects and Spatial Autocorrelations with Equal Weights

Author(s)/Creator(s)

Badi H. Baltagi, Syracuse University. Center for Policy ResearchFollow

Description/Abstract

This note considers a panel data regression model with spatial autoregressive disturbances and random effects where the weight matrix is normalized and has equal elements. This is motivated by Kelejian et al. (2005), who argue that such a weighting matrix, having blocks of equal elements, might be considered when units are equally distant within certain neighborhoods but unrelated between neighborhoods. We derive a simple weighted least squares transformation that obtains GLS on this model as a simple OLS. For the special case of a spatial panel model with no random effects, we obtain two sufficient conditions where GLS on this model is equivalent to OLS. Finally, we show that these results, for the equal weight matrix, hold whether we use the spatial autoregressive specification, the spatial moving average specification, the spatial error components specification or the Kapoor et al. (2005) alternative to modeling panel data with spatially correlated error components.

Document Type

Working Paper

Date

2006

Keywords

Panel data, spatial error correlation, equal weights, error components

Language

English

Series

Working Papers Series

Disciplines

Mathematics

Additional Information

Harvest from RePEc at http://repec.org

Recommended Citation

Baltagi, Badi H., "Random effects and Spatial Autocorrelations with Equal Weights" (2006). Center for Policy Research. 77.
https://surface.syr.edu/cpr/77

Source

Metadata from RePEc

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 License.