Federico NutarelliIMT School for Advanced Studies Lucca · AXES Laboratory for the Analysis of Complex Economic Systems
Federico Nutarelli
Ph.D
Theory and applications of Machine Learning to Economics
About
15
Publications
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Introduction
My research activity focuses on econometrics, health and industrial econmics, and machine learning (theory and applications).
Publications
Publications (15)
Using data on the Spanish firm-level production network we show that firms learn about international trade opportunities and related business know-how from their production network peers. Our identification strategy leverages the panel structure of the data, import origin variation, and network structure. We find evidence of both upstream and downs...
Economic complexity and machine learning have recently
become popular approaches for analysing international
trade. However, for effective use of machine
learning in relation to economic complexity and policymaking,
it is important to understand what are the
key features for predictions. In this framework, this article
addresses the issue of the in...
This paper formalizes smooth curve coloring (i.e., curve identification) in the presence of curve intersections as an optimization problem, and investigates theoretically properties of its optimal solution. Moreover, it presents a novel automatic technique for solving such a problem. Formally, the proposed algorithm aims at minimizing the summation...
This work applies Matrix Completion (MC) – a class of machine-learning methods commonly used in recommendation systems – to analyze economic complexity. In this paper MC is applied to reconstruct the Revealed Comparative Advantage (RCA) matrix, whose elements express the relative advantage of countries in given classes of products, as evidenced by...
This paper formalizes smooth curve coloring (i.e., curve identification) in the presence of curve intersections as an optimization problem, and investigates theoretically properties of its optimal solution. Morever, it presents a novel automatic technique for solving such a problem. Formally, the proposed algorithm aims at minimizing the summation...
The idea that research investments respond to market rewards is well established in the literature on markets for innovation (Schmookler, 1966; Acemoglu and Linn, 2004; Bryan and Williams, 2021). Empirical evidence tells us that a change in market size, such as the one measured by demographical shifts, is associated with an increase in the number o...
This work applies Matrix Completion (MC) -- a class of machine-learning methods commonly used in the context of recommendation systems -- to analyse economic complexity. MC is applied to reconstruct the Revealed Comparative Advantage (RCA) matrix, whose elements express the relative advantage of countries in given classes of products, as evidenced...
We investigate linear regression problems for which one is given the additional possibility of controlling the conditional variance of the output given the input, by varying the computational time dedicated to supervise each example. For a given upper bound on the total computational time for supervision, we optimize the trade-off between the numbe...
This work belongs to the strand of literature that combines machine learning, optimization, and econometrics. The aim is to optimize 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 trainin...
This paper is focused on the unbalanced fixed effects panel data model. This is a linear regression model able to represent unobserved heterogeneity in the data, by allowing each two distinct observational units to have possibly different numbers of associated observations. We specifically address the case in which the model includes the additional...
We investigate regression problems for which one is given the additional possibility of controlling the conditional variance of the output given the input, by varying the computational time dedicated to supervise each example. For a given upper bound on the total computational time, we optimize the trade-off between the number of examples and their...
We investigate a modification of the classical fixed effects panel data model (a linear regression model able to represent unobserved heterogeneity in the data), in which one has the additional possibility of controlling the conditional variance of the output given the input, by varying the cost associated with the supervision of each training exam...