论文标题
机器学习面板数据回归具有重尾依赖数据：理论和应用
Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application
论文作者
论文摘要
该论文介绍了可能在不同频率下采样的重尾依赖面板数据的结构化机器学习回归。我们专注于稀疏组的套管正则化。这种类型的正则化可以利用混合频率时间序列面板数据结构并提高估计值的质量。我们获得了甲骨文的不平等现象，稀疏集团套管面板数据估计值认识到金融和经济数据可以具有脂肪尾巴。为此，我们利用了新的福纳加夫浓度不平等，用于由重型$τ$混合过程组成的面板数据。
The paper introduces structured machine learning regressions for heavy-tailed dependent panel data potentially sampled at different frequencies. We focus on the sparse-group LASSO regularization. This type of regularization can take advantage of the mixed frequency time series panel data structures and improve the quality of the estimates. We obtain oracle inequalities for the pooled and fixed effects sparse-group LASSO panel data estimators recognizing that financial and economic data can have fat tails. To that end, we leverage on a new Fuk-Nagaev concentration inequality for panel data consisting of heavy-tailed $τ$-mixing processes.
