Seemingly Unrelated Regressions with Spatial Error Components

Author(s)/Creator(s)

Badi H. Baltagi, Syracuse University
Alain Pirotte, University of Panthéon-Assas Paris II

Description/Abstract

This paper considers various estimators using panel data seemingly unrelated regressions (SUR) with spatial error correlation. The true data generating process is assumed to be SUR with spatial error of the autoregressive or moving average type. Moreover, the remainder term of the spatial process is assumed to follow an error component structure. Both maximum likelihood and generalized moments (GM) methods of estimation are used. Using Monte Carlo experiments, we check the performance of these estimators and their forecasts under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous versus homogeneous panel data models.

Document Type

Working Paper

Date

9-2010

Keywords

Seemingly unrelated regressions - Panel data - Spatial dependence Heterogeneity Forecasting

Series

Working Papers Series

Disciplines

Economics

Additional Information

Working paper no. 125

Recommended Citation

Baltagi, Badi H. and Pirotte, Alain, "Seemingly Unrelated Regressions with Spatial Error Components" (2010). Center for Policy Research. 166.
https://surface.syr.edu/cpr/166

Source

local input

Creative Commons License

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