用Transformer建模多变量时间序列动态因子，提升小样本预测精度。
Nonlinear Dynamic Factor Analysis With a Transformer Network
- 引入线性因子模型作为先验，通过正则化提升小样本性能。
- 在非线性、非高斯数据下，比线性模型更准确。
- 注意力矩阵可识别变量重要性与状态切换，适合经济分析。
本文提出一种基于Transformer的动态因子估计方法，适用于多变量时间序列，在灵活的识别假设下表现优异。通过在训练目标中加入线性因子模型作为正则项，显著提升了小样本下的性能。注意力矩阵用于量化变量及其滞后项对因子估计的重要性，其时变特征有助于识别经济状态转换并验证经济叙事。蒙特卡洛实验表明，当数据偏离线性高斯假设时，该Transformer模型比传统线性因子模型更准确。实证应用中，利用该模型构建了美国实际经济活动的同步指数。
原文摘要 · Abstract (English)
The paper develops a Transformer architecture for estimating dynamic factors from multivariate time series data under flexible identification assumptions. Performance on small datasets is improved substantially by using a conventional factor model as prior information via a regularization term in the training objective. The results are interpreted with Attention matrices that quantify the relative importance of variables and their lags for the factor estimate. Time variation in Attention patterns can help detect regime switches and evaluate narratives. Monte Carlo experiments suggest that the Transformer is more accurate than the linear factor model, when the data deviate from linear-Gaussian assumptions. An empirical application uses the Transformer to construct a coincident index of U.S. real economic activity.
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