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

Seonkyu Lim, Jeongwhan Choi, Noseong Park, Sang-Ha Yoon, ShinHyuck Kang, Young-Min Kim, Hyunjoong Kang

TL;DR

NCDENow is introduced, a novel GDP nowcasting framework that integrates neural controlled differential equations (NCDEs) with DFMs and effectively handles the dynamics of irregular time series, highlighting the significant potential of integrating NCDE into nowcasting models.

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.

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

TL;DR

NCDENow is introduced, a novel GDP nowcasting framework that integrates neural controlled differential equations (NCDEs) with DFMs and effectively handles the dynamics of irregular time series, highlighting the significant potential of integrating NCDE into nowcasting models.

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.
Paper Structure (26 sections, 11 equations, 5 figures, 6 tables, 1 algorithm)

This paper contains 26 sections, 11 equations, 5 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries & Related Work
  3. GDP Nowcasting
  4. Dynamic Factor Models
  5. Neural Controlled Differential Equations
  6. Proposed Method
  7. Overall Workflow
  8. Exposure Estimation Module
  9. Factor Extraction Module
  10. Regression Module
  11. How to Train
  12. Well-posedness of NCDENow
  13. Properties of NCDENow
  14. Experiments
  15. Experimental Settings
  16. ...and 11 more sections

Figures (5)

  • Figure 1: Nowcasting vs. ground-truth on South Korea GDP during COVID-19. NCDENow, our proposed method, captures sudden drops better than DFM.
  • Figure 2: Example of GDP nowcasting. The macroeconomic indicators have multi-frequencies. We want to estimate third-quarter (Q3) GDP growth, but the first estimates are unavailable until the end of October. In this case, we nowcast GDP growth using other indicators released in Q3.
  • Figure 3: NCDENow workflow illustrating GDP nowcasting for Q4 2021 using macroeconomic indicators from Q4 2020 to Q4 2021.
  • Figure 4: Estimated latent factors from the South Korea GDP dataset. Gray shading indicates the 11th business cycle and COVID-19 economic shock.
  • Figure 5: MAPE versus number of parameters