Luca Alfieri will defend his doctoral thesis „Forecasting Business Cycles and Financial Crises with Machine Learning“
On 19 May at 14:00 Luca Alfieri will defend his doctoral thesis „Forecasting business cycles and financial crises with machine learning“ for obtaining the degree of Doctor of Philosophy (in Economics)
Supervisors:
Associate Professor Jaan Masso (PhD), University of Tartu
Associate Professor Lenno Uusküla (PhD), University of Tartu
Opponents:
Associate Professor Christian Brownlees (PhD), Universitat Pompeu Fabra, Spain
Dr. Dmitry Kulikov (PhD), Eesti Pank (Bank of Estonia), Estonia
Summary
This thesis explores the application of machine learning methods in macroeconomic forecasting, focusing on business cycles and the prediction of systemic financial crises. It investigates two key approaches:. The study integrates a data-driven method that leverages a large set of variables to capture economic nonlinearities and a theory-based approach that selects variables based on macroeconomic principles by combining theory-driven variable selection with a broad range of predictors.
The research underscores the significance of addressing imbalanced data, selecting relevant features, and accounting for nonlinearities in machine learning models. It suggests that boosting methods can be effective when dealing with large datasets, although they require careful preparation, especially when the number of data is limited. The thesis also highlights the difficulties of crisis prediction using historical data and emphasizes the need for further research on model optimization.
Overall, the thesis demonstrates how machine learning can enhance macroeconomic forecasting by merging modern machine learning techniques with traditional econometric approaches. It stresses the importance of critically evaluating data and methodologies to ensure that machine learning models complement rather than replace conventional methods. While the rise of Big Data has increased the appeal of machine learning, relying on it blindly or discarding traditional approaches is not advisable. Instead, machine learning models tend to perform best when combined with econometric techniques, as they share common statistical foundations, making many new methods more closely linked to traditional ones than they initially appear.
The defence will be held in Narva Rd. 18–1018 and online
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