A spatial autoregressive Poisson gravity model

Auteurs :
SELLNER, R.
FISCHER, M.M.
KOCH, M.

Description :
A Poisson gravity model is introduced that incorporates spatial dependence of the explained variable without relying on restrictive distributional assumptions of the underlying data-generating process. The AA. derive a two-stage nonlinear least-squares estimator that is hetero-scedasticity-robust and controls for the problem of over- or underdispersion that often is present in the empirical analysis of discrete data . In the model specification, the resulting parameter estimates can be interpreted as the implied total impact effects defined as the sum of direct and indirect spatial feedback effects. The model is illustrated by analyzing patent citation flows data across European regions.


Type de document :
Article de périodique

Source :
Geographical analysis, issn : 0016-7363, 2013, vol. 45, n°. 2, p. 180-201, nombre de pages : 22, Références bibliographiques : 2 p.

Date :
2013

Editeur :
Pays édition : Etats-Unis, Columbus, OH, Ohio State University Press