On Quantile Regression Forests for Modelling Mixed-Frequency and Longitudinal Data
It addresses the need for non-parametric quantile estimation in complex data settings like finance and climate science, but is incremental as it extends existing methods to new data types.
This thesis tackled the problem of applying Quantile Regression Forests to mixed-frequency and longitudinal data by developing two new algorithms, MIDAS-QRF and FM-QRF, which were validated as accurate and flexible models in financial risk management and climate-change impact evaluation.
The aim of this thesis is to extend the applications of the Quantile Regression Forest (QRF) algorithm to handle mixed-frequency and longitudinal data. To this end, standard statistical approaches have been exploited to build two novel algorithms: the Mixed- Frequency Quantile Regression Forest (MIDAS-QRF) and the Finite Mixture Quantile Regression Forest (FM-QRF). The MIDAS-QRF combines the flexibility of QRF with the Mixed Data Sampling (MIDAS) approach, enabling non-parametric quantile estimation with variables observed at different frequencies. FM-QRF, on the other hand, extends random effects machine learning algorithms to a QR framework, allowing for conditional quantile estimation in a longitudinal data setting. The contributions of this dissertation lie both methodologically and empirically. Methodologically, the MIDAS-QRF and the FM-QRF represent two novel approaches for handling mixed-frequency and longitudinal data in QR machine learning framework. Empirically, the application of the proposed models in financial risk management and climate-change impact evaluation demonstrates their validity as accurate and flexible models to be applied in complex empirical settings.