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.

Effects of Transport Improvements on Commuting and Residential Choice

This paper develops a commuter location model able to explain and simulate residential location changes of commuters that result from transport improvements. The core model is based on the assumption of constant commuting time, while two extensions incorporate substitution possibilities having an upward effect on total commuting time. Estimation errors of the residential location of the working population with the existing transport system are limited to 7%. With the extended model, the impacts on commuting and residential choice are investigated for six higher speed rail connections between Amsterdam, located in the urban core of the Netherlands, and Groningen, located in its rural periphery. The model outcomes strongly influenced the public policy debate in the Netherlands.

The mystery of regional unemployment differentials:a survey of theoretical and empirical explanations

This paper attempts to provide an integrated overview of theoretical and empirical explanations used in the applied literature on regional unemployment differentials. On the basis of 41 empirical studies, four different model types covering nine theoretical constructs of regional unemployment determination and 13 sets of explanatory variables are identified. The overall conclusion is that theoretical and empirical explanations help to reduce the weaknesses in each other. While theory is found to predict that the regional unemployment rate depends on labour supply factors (a collection of factors which affect natural changes in the labour force, labour force participation, migration and commuting), labour demand factors and wage-setting factors, it is the empirical studies that gain a more profound understanding of the explanatory variables involved. Conversely, whereas most empirical studies provide clear-cut explanations for the signs of the explanatory variables, it is theory that shows that some of these explanations might be out of proportion. By grouping many studies together, this paper shows that there are indeed clear-cut trends