用分组稀疏自编码改进经济预测模型，提升可解释性与准确性。
Time-varying Factor Augmented Vector Autoregression with Grouped Sparse Autoencoder
- 引入分组稀疏自编码器，通过共用经济类别参数增强可解释性。
- 结合时变参数VAR，点预测和密度预测均优于传统模型。
- 适合关注宏观经济建模与政策分析的研究者使用。
近期全球金融危机和新冠疫情暴露了线性因子增广向量自回归（FAVAR）模型在预测与结构分析中的局限性。非线性降维技术，尤其是自编码器，在FAVAR框架中展现出潜力，但存在可识别性、可解释性及与传统非线性时间序列方法整合的挑战。本文提出两项贡献：首先，引入采用Spike-and-Slab Lasso先验的分组稀疏自编码器，同一经济类别的变量共享参数，实现半可识别性并提升模型可解释性；其次，将时变参数引入VAR部分，更好捕捉经济动态变化。对美国经济的实证应用表明，该分组稀疏自编码器通过简洁结构生成更可解释的因子；其与时变参数VAR结合，在点预测与密度预测上表现更优。脉冲响应分析显示，衰退期货币政策冲击引发的反应较温和且不确定性更高，相较扩张期。
原文摘要 · Abstract (English)
Recent economic events, including the global financial crisis and COVID-19 pandemic, have exposed limitations in linear Factor Augmented Vector Autoregressive (FAVAR) models for forecasting and structural analysis. Nonlinear dimension techniques, particularly autoencoders, have emerged as promising alternatives in a FAVAR framework, but challenges remain in identifiability, interpretability, and integration with traditional nonlinear time series methods. We address these challenges through two contributions. First, we introduce a Grouped Sparse autoencoder that employs the Spike-and-Slab Lasso prior, with parameters under this prior being shared across variables of the same economic category, thereby achieving semi-identifiability and enhancing model interpretability. Second, we incorporate time-varying parameters into the VAR component to better capture evolving economic dynamics. Our empirical application to the US economy demonstrates that the Grouped Sparse autoencoder produces more interpretable factors through its parsimonious structure; and its combination with time-varying parameter VAR shows superior performance in both point and density forecasting. Impulse response analysis reveals that monetary policy shocks during recessions generate more moderate responses with higher uncertainty compared to expansionary periods.
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