An Application of Graphical Models to the Innobarometer Survey: A Map of Firms’ Innovative Behaviour

Abstract

Probabilistic graphical models successfully combine probability with graph theory and therefore provide applied statisticians with a powerful data mining engine. Graphical models are a good framework for formal analysis, allowing the researcher to obtain a quick overview of the structure of association among variables in a system. This paper is the first attempt to apply high-dimensional graphical models in innovation studies, since the i ncreasing availability of data in the field and the complexity of the underlying processes are calling for new techniques which can handle not only a large amount of observations, but also rich datasets in terms of number and relations among variables. In this context, the process of variables and model selection became more arduous, influenced by biases of the scientist and, in the worst case scenario, subject to scientific malpractices such as the p-hacking behavior. On the contrary, high-dimensional graphical models allow for bottom-up, hypotheses free, data-driven, and see-through approach.