Phd in ECONOMICS

Thesis title: Essays on Machine Learning approaches to Macroeconomic Modeling

The thesis is an in-depth examination of the potential of machine learning and artificial intelligence techniques to improve the accuracy of macroeconomic forecasting and real-time tracking of economic activity. The research seeks to understand how these innovative methods can be used to provide more precise and up-to-date information about the state of the economy, thus allowing for better predictions of macroeconomic trends. The study will focus on different machine learning and econometric approaches, ranging from neural networks to time series models, and incorporate various data types, including economic indicators and financial market data. The aim is to provide economists and policymakers with the tools they need to make informed economic policy decisions and help businesses and investors make wise investments and strategic decisions. This research is a valuable contribution to the field of macroeconomic forecasting, helping to improve the accuracy of predictions and providing stakeholders with the information they need to make well-informed decisions. By using state-of-the-art techniques from machine learning and artificial intelligence, this thesis can significantly enhance the understanding and governance of the economy.