Unconditional maximum likelihood estimation of dynamic models for spatial panels
AbstractThis 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.
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Bibliographic InfoPaper provided by University of Groningen, Research Institute SOM (Systems, Organisations and Management) in its series Research Report with number 03C27.
Date of creation: 2003
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-GEO-2003-09-24 (Economic Geography)
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