解决高维时间序列预测过拟合问题，提升冲击响应估计稳定性。
Enhanced Random Subspace Local Projections for High-Dimensional Time Series Analysis
- 通过加权子空间聚合与自适应大小选择增强子空间采样。
- 在长预测期下使估计器方差降低33%，置信区间更窄14%。
- 适合处理海量相关变量的宏观经济分析，如FRED-MD数据集。
高维时间序列预测在预测变量数量超过观测样本时易出现严重过拟合，导致传统局部投影方法不稳定。本文提出增强型随机子空间局部投影（RSLP）框架，通过加权子空间聚合、类别感知子空间采样、自适应子空间大小选择及针对依赖数据设计的自助推断程序，显著提升长期预测期下的估计稳定性与有限样本推断可靠性。在模拟数据、宏观经济指标及包含126个预测变量的FRED-MD数据集上的实验表明，自适应子空间大小选择使预测期h ≥ 3时估计器方差减少33%；自助推断程序在高维设定下生成更保守且宽度缩小14%的置信区间，同时保持正确覆盖概率。该框架为实践者提供了一种可信赖的方法，将丰富信息集纳入冲击响应分析而不受传统高维方法不稳定的困扰。
原文摘要 · Abstract (English)
High-dimensional time series forecasting suffers from severe overfitting when the number of predictors exceeds available observations, making standard local projection methods unstable and unreliable. We propose an enhanced Random Subspace Local Projection (RSLP) framework designed to deliver robust impulse response estimation in the presence of hundreds of correlated predictors. The method introduces weighted subspace aggregation, category-aware subspace sampling, adaptive subspace size selection, and a bootstrap inference procedure tailored to dependent data. These enhancements substantially improve estimator stability at longer forecast horizons while providing more reliable finite-sample inference. Experiments on synthetic data, macroeconomic indicators, and the FRED-MD dataset demonstrate a 33 percent reduction in estimator variability at horizons h >= 3 through adaptive subspace size selection. The bootstrap inference procedure produces conservative confidence intervals that are 14 percent narrower at policy-relevant horizons in very high-dimensional settings (FRED-MD with 126 predictors) while maintaining proper coverage. The framework provides practitioners with a principled approach for incorporating rich information sets into impulse response analysis without the instability of traditional high-dimensional methods.
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