FEDERICO D'AMARIO

Dottore di ricerca

ciclo: XXXVI


supervisore: Massimiliano Tancioni

Titolo della tesi: Essays on Machine Learning approaches to Causality and Forecasting

This thesis offers an extensive and detailed examination of machine learning estimators, coupled with innovative data sources and auxiliary techniques, within the context of forecasting and causal analysis. Across three comprehensive chapters, it delves into an analysis of current literature, presents original concepts and methodologies, and sheds light on the potential effects and implications these techniques may have in their respective fields, particularly through the lens of empirical applications. The objective of this work is to serve as an invaluable resource for a diverse audience, including researchers, policymakers, and anyone interested in gaining a deeper understanding of the dynamic and evolving nature of forecasting and causal inference in an era characterised by an ever-increasing abundance of data and ever-growing and pivotal role of machine learning in advancing the field of econometrics.

Produzione scientifica

11573/1726255 - 2024 - Inflationary shocks, economic aggregates and households’ green transition. A causal machine learning analysis using mixed frequency data
Ciganovic, Milos; D'amario, Federico; Tancioni, Massimiliano - 01a Articolo in rivista
rivista: Social Situation Monitor (Publications Office of the European Union) pp. 1-44 - issn: 2811-6798 - wos: (0) - scopus: (0)

11573/1697776 - 2023 - Forecasting cryptocurrencies log-returns. A LASSO-VAR and sentiment approach
Ciganovic, M.; D'amario, F. - 01a Articolo in rivista
rivista: APPLIED ECONOMICS (Abingdon, UK: Routledge -Taylor & Francis Group) pp. - - issn: 1466-4283 - wos: WOS:001126660300001 (0) - scopus: 2-s2.0-85179923995 (0)

11573/1649000 - 2020 - The US labor share: a TVC-SVAR approach to the causes of decline
D'amario, Federico - 02a Capitolo o Articolo
libro: Advances in economics: research at the DED 2019 - ()

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