Coraggio, Luca (2019) Machine Learning methods and applications in Economics. [Tesi di dottorato]
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| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | Machine Learning methods and applications in Economics |
| Autori: | Autore Email Coraggio, Luca lucacoraggio@hotmail.com |
| Data: | 11 Dicembre 2019 |
| Numero di pagine: | 167 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Scienze Economiche e Statistiche |
| Dottorato: | Economia |
| Ciclo di dottorato: | 32 |
| Coordinatore del Corso di dottorato: | nome email Pagano, Marco marco.pagano@unina.it |
| Tutor: | nome email Pagano, Marco [non definito] Coretto, Pietro [non definito] |
| Data: | 11 Dicembre 2019 |
| Numero di pagine: | 167 |
| Parole chiave: | Machine Learning; Machine Learning Econometrics; Machine Learning Economics; Cluster Analysis; Model-based; Bootstrap; Bootstrap Clustering; Scoring; Employees to tasks assignments; Employees allocation; Sorting; Job Assignment Quality; JAQ |
| Settori scientifico-disciplinari del MIUR: | Area 13 - Scienze economiche e statistiche > SECS-P/05 - Econometria Area 13 - Scienze economiche e statistiche > SECS-P/06 - Economia applicata Area 13 - Scienze economiche e statistiche > SECS-S/01 - Statistica |
| Depositato il: | 14 Gen 2020 16:40 |
| Ultima modifica: | 17 Nov 2021 11:46 |
| URI: | http://www.fedoa.unina.it/id/eprint/12984 |
Abstract
The thesis introduces supervised and unsupervised learning concepts having in mind an unexperienced audience, pointing out relevant references for further studies. Moreover, we highlight the relevance of Machine Learning for Economics and what are the possible applications. Then, the work proceeds with two contributions. The first one is a methodological contribution to cluster analysis; here we propose a novel method to score and evaluate clustering solutions where clusters are parametrized by centres, scatters and sizes parameters. The second contribution is an application of Machine Lerning methods to Labor Economics. We explore the assignment of employees-to-tasks and use trees-based learning algorithms to retrieve a mapping for the assignment. We show that the so-derived assignment rule helps explaining productivity drivers.
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