At the intersection between Machine Learning and Econometrics: theory and applications

In the present work, we introduce theoretical and application novelties at the intersection between machine learning and econometrics in social and health sciences. In particular, Part 1 delves into optimizing the data collection process in a specific statistical model, commonly used in econometrics, employing an optimization criterion inspired by machine learning, namely, the generalization error conditioned on the training input data. In the first Chapter, we analyze and optimize the trade-off between sample size, the precision of supervision on a variation of the unbalanced fixed effects panel data model. In the second Chapter we extend the analysis to the Fixed Effects GLS (FEGLS) case in order to account for the heterogeneity in the data associated with different units, for which correlated measurement errors corrupt distinct observations related to the same unit. In Part 2, we introduce applications of innovative econometrics and machine learning techniques. In the third Chapter we propose a novel methodology to explore the effect of market size on market innovation in the Pharmaceutical industry. Finally, in the fourth Chapter, we innovate the literature on the economic complexity of countries through machine learning. The Dissertation contributes to the literature on machine learning and applied econometrics mainly by: (i) extending the current framework to novel scenarios and applications (Chapter 1 - Chapter 2); (ii) developing a novel econometric methodology to assess long-debated issues in literature (Chapter 3); (iii) constructing a novel index of economic complexity through machine learning (Chapter 4).