Unconditional maximum likelihood estimation of dynamic models for spatial panels

This paper hammers out the estimation of a fixed effects dynamic panel data model extended
either to include 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 of both models is found to exist. When exogenous variables are included, the presample
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 American states over the period 1963 to 1992.

	03c27.pdf

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