Marta Bisztray (p)

Paul Elhorst

see document

Giulio Breglia (p)

Marta Bisztray

see extended abstract

Paul Elhorst (p), Jean-Loup Madre , Alain Pirotte

Giulio Breglia

This paper adopts a dynamic spatial panel data model with common factors to explore the effect of population density, real household income per capita, car fleet per capita, and real price of gasoline on regional traffic per light vehicle at the NUTS3 level in France over the period 1990-2009. Spatial heterogeneity is modeled by a translog function in the first three explanatory variables, which are dominated by variation in the cross-sectional domain, while the real price of gasoline, which is dominated by variation in the time domain, is treated as an observable common factor. Unobservable common factors are controlled for by modeling them as principal components with regional-specific coefficients. This setup generalizes the dynamic spatial panel data model with regional and time period fixed applied in recent studies. It is found that local spatial lags in the dependent variable and the error term almost vanish when allowing for spatial heterogeneity, i.e, when extending the model from a linear to a quadratic functional form, while spatial lags in the explanatory remain significant. This paper explains the wider implications of this finding for spatial econometric modeling. The marginal effects of the first three explanatory variables, are shown to vary across space and time and to follow a plausible structure.