Revisiting the Spatial Autoregressive Exponential Model for Counts and Other Nonnegative Variables, with Application to the Knowledge Production Function

This paper proposes a two-step pseudo-maximum likelihood estimator of a spatial autoregressive exponential model for counts and other nonnegative variables; it is particularly useful for dealing with zeros. It considers a model specification allowing us to easily determine the direct and indirect partial effects of explanatory variables (spatial spillovers and externalities). A simulation study shows that this method generally behaves better in terms of bias and root mean square error than existing procedures. An empirical example estimating a knowledge production function for the NUTS II European regions is analyzed. Results show that there is spatial dependence between regions on the creation of innovation, where regions less able to transform R&D expenses into innovation benefit from knowledge spatial spillovers through indirect effects. It is also concluded that the socioeconomic environment is important and that, unlike public R&D institutions, private companies are efficient at knowledge production.

Automated summary

This paper introduces a new estimation method that can better handle models for counts and nonnegative variables, demonstrating its superiority through simulation studies and empirical examples. Results show spatial dependence between regions and the significant impact of the socioeconomic environment on knowledge production.