Essays on Machine Learning in Economics

Abstract

This thesis presents three studies exploring how machine learning can be applied to understand and model complex phenomena in economics. Chapter 2 investigates how machine learning can be used to examine geographic diversity within global economics research. Chapter 3, published in Energy Economics (2024), develops a seasonal deep learning model for Great Britain’s electricity imbalance prices, showing that incorporating seasonality improves forecasting accuracy. Chapter 4 extends this framework by applying distributional deep learning methods to model full price distributions and distinguish between aleatoric and epistemic uncertainty.