Targeting Predictors Via Partial Distance Correlation With Applications to Financial Forecasting
基于偏距离相关的预测变量筛选及其金融预测应用
High-dimensional time series datasets are becoming increasingly common in various fields of economics and finance. Given the ubiquity of time series data, it is crucial to develop efficient variable screening methods that use the unique features of time series. This article introduces several model-free screening methods based on partial distance correlation and developed specifically to deal with time-dependent data. Methods are developed both for univariate models, such as…