Titolo della tesi: Dynamic Factor Models: improvements and applications
In the last two decades data collection, aided by an increased computational capability, has considerably increased both dimension and structure of the datasets; given this, statisticians and economists may today work with time series of remarkable dimension which may come from different sources. Dealing with such datasets may not be so easy and requires the development of ad hoc mathematical models.
Dynamic Factor Models (DFM) represent one of the newest techniques in big data management. The adoption of those models allowed me to deepen the study of volatility while introducing Bayesian non-parametric techniques, and to do structural analysis improving the generated impulse response functions.
The application of this all was made in the field of economics and finance.