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

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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.

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  • Carota, Cinzia & Durio, Alessandra & Guerzoni, Marco, 2014. "An Application of Graphical Models to the Innobarometer Survey: A Map of Firms’ Innovative Behaviour," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201444, University of Turin.
  • Handle: RePEc:uto:dipeco:201444
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    1. Marco Guerzoni, 2010. "The impact of market size and users' sophistication on innovation: the patterns of demand," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 19(1), pages 113-126.
    2. Roberto Fontana & Marco Guerzoni, 2008. "Incentives and uncertainty: an empirical analysis of the impact of demand on innovation," Cambridge Journal of Economics, Oxford University Press, vol. 32(6), pages 927-946, November.
    3. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Bayesian Graphical Models for STructural Vector Autoregressive Processes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 357-386, March.
    4. Mowery, David & Rosenberg, Nathan, 1993. "The influence of market demand upon innovation: A critical review of some recent empirical studies," Research Policy, Elsevier, vol. 22(2), pages 107-108, April.
    5. Abreu, Gabriel C. G. & Labouriau, Rodrigo & Edwards, David, 2010. "High-Dimensional Graphical Model Search with the gRapHD R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 37(i01).
    6. Guerzoni, Marco & Raiteri, Emilio, 2015. "Demand-side vs. supply-side technology policies: Hidden treatment and new empirical evidence on the policy mix," Research Policy, Elsevier, vol. 44(3), pages 726-747.
    7. Acs, Zoltan J & Audretsch, David B, 1987. "Innovation, Market Structure, and Firm Size," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 567-574, November.
    8. Daniel Felix Ahelegbey & Paolo Giudici, 2014. "Hierarchical Graphical Models, With Application To Systemic Risk," DEM Working Papers Series 063, University of Pavia, Department of Economics and Management.
    9. Anthony Arundel & Aldo Geuna, 2004. "Proximity and the use of public science by innovative European firms," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 13(6), pages 559-580.
    10. Freeman, Chris, 1994. "The Economics of Technical Change," Cambridge Journal of Economics, Oxford University Press, vol. 18(5), pages 463-514, October.
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