Revisiting the spatial autoregressive exponential model for counts and other nonnegative variables, with application to the knowledge production function

• Autor(a) principal: Proença, Isabel
• Data de Publicação: 2021
• Outros Autores: Glórias, Ludgero
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.5/28079
• topic: Spatial Autoregressive Exponential Regression; Poisson Pseudo-Maximum Likelihood Estimator; Two-Step Limited Information Maximum Likelihood; Spatial Spillovers; Knowledge Production

This paper proposes a two-step pseudo-maximum likelihood estimator of a spatial auto regressive 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.