Panel Data Inference under Spatial Dependence

Author(s)/Creator(s)

Badi H. Baltagi, Syracuse UniversityFollow
Alain Pirotte, Université Panthéon-Assas Paris II

Description/Abstract

This paper focuses on inference based on the usual panel data estimators of a one-way error component regression model when the true specification is a spatial error component model. Among the estimators considered, are pooled OLS, random and fixed effects, maximum likelihood under normality, etc. The spatial effects capture the cross-section dependence, and the usual panel data estimators ignore this dependence. Two popular forms of spatial autocorrelation are considered, namely, spatial auto-regressive random effects (SAR-RE) and spatial moving average random effects (SMA-RE). We show that when the spatial coefficients are large, test of hypothesis based on the usual panel data estimators that ignore spatial dependence can lead to misleading inference.

Document Type

Working Paper

Date

2010

Keywords

Panel data, Hausman test, Random effect, Spatial autocorrelation, Maximum Likelihood, Center for Policy ResearchWorking Paper No. 123

Language

English

Series

Working Papers Series

Disciplines

Public Policy

Additional Information

Working paper no. 123

Harvest from RePEc at http://repec.org

Recommended Citation

Baltagi, Badi H. and Pirotte, Alain, "Panel Data Inference under Spatial Dependence" (2010). Center for Policy Research. 43.
https://surface.syr.edu/cpr/43

Source

Metadata from RePEc

Creative Commons License

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