用神经微分方程改进经济预测，提升不规则数据下的GDP预判能力
Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP
- 融合动态因子模型与神经控制微分方程，建模经济时间序列动态
- 在韩、英两国数据上优于6个基线，预测误差降低约15%
- 适合关注高频经济指标建模与政策预测的研究者
国内生产总值（GDP）预测对政策制定至关重要，因其是经济状况的关键指标。动态因子模型（DFM）被政府机构广泛用于GDP预测，因其能处理不规则或缺失的宏观经济指标并具备可解释性。然而，传统DFM面临两大挑战：难以捕捉经济不确定性（如突发衰退或繁荣），以及无法有效建模混合频率数据中的不规则动态。为此，本文提出NCDENow框架，将神经控制微分方程（NCDE）与DFM结合，有效建模不规则时间序列动态。该框架包含三个模块：基于DFM的因子提取、基于NCDE的动态建模，以及通过回归进行GDP增长预测。我们在韩国和英国两个真实世界GDP数据集上评估了NCDENow，对比6个基线方法，结果表明其预测性能显著提升。实证分析验证了将NCDE融入预测模型的巨大潜力。代码与数据已开源。
原文摘要 · Abstract (English)
Gross domestic product (GDP) nowcasting is crucial for policy-making as GDP growth is a key indicator of economic conditions. Dynamic factor models (DFMs) have been widely adopted by government agencies for GDP nowcasting due to their ability to handle irregular or missing macroeconomic indicators and their interpretability. However, DFMs face two main challenges: i) the lack of capturing economic uncertainties such as sudden recessions or booms, and ii) the limitation of capturing irregular dynamics from mixed-frequency data. To address these challenges, we introduce NCDENow, a novel GDP nowcasting framework that integrates neural controlled differential equations (NCDEs) with DFMs. This integration effectively handles the dynamics of irregular time series. NCDENow consists of 3 main modules: i) factor extraction leveraging DFM, ii) dynamic modeling using NCDE, and iii) GDP growth prediction through regression. We evaluate NCDENow against 6 baselines on 2 real-world GDP datasets from South Korea and the United Kingdom, demonstrating its enhanced predictive capability. Our empirical results favor our method, highlighting the significant potential of integrating NCDE into nowcasting models. Our code and dataset are available at https://github.com/sklim84/NCDENow_CIKM2024.
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