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<p>The Vector AutoRegressive (VAR) model is widely used in macroeconomic modeling but struggles with the curse of dimensionality. The Factor-Augmented VAR (FAVAR) model addresses this by extracting latent factors, typically via Principal Component Analysis (PCA), yet remains constrained by linear structures and limited adaptability to high-dimensional, nonlinear macroeconomic relationships. To overcome these challenges, this study proposes a Gromov-Wasserstein Autoencoder (GWAE)-FAVAR framework, integrating optimal transport theory to improve factor extraction while preserving intrinsic economic structures. Empirical analysis on U.S. macroeconomic data-including energy, inflation, national accounts, labor markets, and government finance-demonstrates that GWAE-FAVAR enhances factor interpretability and predictive accuracy. Additionally, we introduce a Transformer-Enhanced VAR, leveraging self-attention mechanisms to capture long-term dependencies and nonlinear interactions in macro-financial data. Results indicate that our approach significantly improves forecasting performance, particularly during economic shocks and structural shifts. This study bridges econometrics and machine learning, advancing macroeconomic modeling through geometric deep learning. The proposed framework offers a robust and interpretable alternative to traditional factor models, enhancing macroeconomic inference and forecasting.</p>

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Beyond Traditional Factor Models: Can Gromov-Wasserstein Improve Macroeconomic Dimensionality Reduction?

  • 期刊论文

  • 发表日期：

    2026年4月15日

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  • Xiaochao Xing,
  • Lu Wang

摘要

The Vector AutoRegressive (VAR) model is widely used in macroeconomic modeling but struggles with the curse of dimensionality. The Factor-Augmented VAR (FAVAR) model addresses this by extracting latent factors, typically via Principal Component Analysis (PCA), yet remains constrained by linear structures and limited adaptability to high-dimensional, nonlinear macroeconomic relationships. To overcome these challenges, this study proposes a Gromov-Wasserstein Autoencoder (GWAE)-FAVAR framework, integrating optimal transport theory to improve factor extraction while preserving intrinsic economic structures. Empirical analysis on U.S. macroeconomic data-including energy, inflation, national accounts, labor markets, and government finance-demonstrates that GWAE-FAVAR enhances factor interpretability and predictive accuracy. Additionally, we introduce a Transformer-Enhanced VAR, leveraging self-attention mechanisms to capture long-term dependencies and nonlinear interactions in macro-financial data. Results indicate that our approach significantly improves forecasting performance, particularly during economic shocks and structural shifts. This study bridges econometrics and machine learning, advancing macroeconomic modeling through geometric deep learning. The proposed framework offers a robust and interpretable alternative to traditional factor models, enhancing macroeconomic inference and forecasting.