We investigate the relationship between “Venture Capital Expenditures” and innovation in Europe. Data are collected from the European Innovation Scoreboard for 36 countries in the period 2010-2019. We perform Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, Dynamic Panel. Results show that the level of Venture Capitalist Expenditure is positively associated to “Foreign Doctorate Students” and “Innovation Index” and negatively related to “Government Procurement of Advanced Technology Products”, “Innovators”, “Medium and High-Tech Products Exports”, “Public-Private Co-Publications”. In adjunct, cluster analysis is realized with the algorithm k-Means and the Silhouette coefficient, and we found the presence of four different clusters for the level of “Venture Capital Expenditures”. Finally, we propose a confrontation among 8 different algorithms of machine learning to predict the level of “Venture Capital Expenditures” and we find that the linear regression generates the best results in terms of minimization of MAE, MSE, RMSE.

The Impact of Venture Capital Expenditures on Innovation in Europe

Alberto Costantiello
;
Lucio Laureti
2021

Abstract

We investigate the relationship between “Venture Capital Expenditures” and innovation in Europe. Data are collected from the European Innovation Scoreboard for 36 countries in the period 2010-2019. We perform Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, Dynamic Panel. Results show that the level of Venture Capitalist Expenditure is positively associated to “Foreign Doctorate Students” and “Innovation Index” and negatively related to “Government Procurement of Advanced Technology Products”, “Innovators”, “Medium and High-Tech Products Exports”, “Public-Private Co-Publications”. In adjunct, cluster analysis is realized with the algorithm k-Means and the Silhouette coefficient, and we found the presence of four different clusters for the level of “Venture Capital Expenditures”. Finally, we propose a confrontation among 8 different algorithms of machine learning to predict the level of “Venture Capital Expenditures” and we find that the linear regression generates the best results in terms of minimization of MAE, MSE, RMSE.
Innovation and Invention: Processes and Incentives, Management of Technological Innovation and R&D, Technological Change: Choices and Consequences, Intellectual Property and Intellectual Capital, Open Innovation, Government Policy.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12572/7305
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