提出两种新算法，用非参数方法处理不同频率和纵向数据的分位数预测。
On Quantile Regression Forests for Modelling Mixed-Frequency and Longitudinal Data
- 融合QRF与MIDAS思想，实现多频数据非参数分位数建模。
- 引入随机效应机制，支持纵向数据的条件分位数估计。
- 在金融风险与气候变化评估中表现准确，适合复杂实证场景。
本论文旨在将分位数回归森林（QRF）算法扩展至混合频率和纵向数据的建模。为此，借鉴经典统计方法，提出了两种新算法：混合数据采样分位数回归森林（MIDAS-QRF）与有限混合分位数回归森林（FM-QRF）。MIDAS-QRF结合了QRF的灵活性与混合数据采样（MIDAS）方法，可在变量观测频率不同时实现非参数分位数估计；FM-QRF则将随机效应机器学习算法拓展至分位数回归框架，支持纵向数据下的条件分位数建模。本研究在方法上贡献了两种新的分位数回归机器学习框架，用于处理混合频率与纵向数据。实证上，所提模型在金融风险管理和气候变化影响评估中表现出高精度与强适应性，验证了其在复杂实证场景中的有效性。
原文摘要 · Abstract (English)
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.
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