2 research outputs found
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\ud
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)