Unconditional Maximum Likelihood Estimation of Linear and Log-Linear Dynamic Models for Spatial Panels
Article first published online: 21 DEC 2004
Volume 37, Issue 1, pages 85–106, January 2005
How to Cite
Elhorst, J. P. (2005), Unconditional Maximum Likelihood Estimation of Linear and Log-Linear Dynamic Models for Spatial Panels. Geographical Analysis, 37: 85–106. doi: 10.1111/j.1538-4632.2005.00577.x
- Issue published online: 21 DEC 2004
- Article first published online: 21 DEC 2004
- Submitted: July 11, 2003. Revised version accepted: May 18, 2004.
This article hammers out the estimation of a fixed effects dynamic panel data model extended to include either spatial error autocorrelation or a spatially lagged dependent variable. To overcome the inconsistencies associated with the traditional least-squares dummy estimator, the models are first-differenced to eliminate the fixed effects and then the unconditional likelihood function is derived taking into account the density function of the first-differenced observations on each spatial unit. When exogenous variables are omitted, the exact likelihood function is found to exist. When exogenous variables are included, the pre-sample values of these variables and thus the likelihood function must be approximated. Two leading cases are considered: the Bhargava and Sargan approximation and the Nerlove and Balestra approximation. As an application, a dynamic demand model for cigarettes is estimated based on panel data from 46 U.S. states over the period from 1963 to 1992.