Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP

TL;DR

This paper introduces NCDENow, a novel GDP nowcasting framework that combines dynamic factor models with neural controlled differential equations to better handle irregular data and economic uncertainties, improving prediction accuracy.

Contribution

The paper presents a new integration of NCDEs with DFMs for GDP nowcasting, addressing limitations in capturing irregular dynamics and uncertainties in economic data.

Findings

NCDENow outperforms 6 baseline models on real GDP datasets.

The framework effectively captures irregular time series dynamics.

Empirical results show improved predictive accuracy.

Abstract

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.