Time-varying Factor Augmented Vector Autoregression with Grouped Sparse Autoencoder

2025-03-06

AI Summary

Traditional linear FAVAR models struggle to capture nonlinear dynamics during crises and suffer from limited interpretability and structural identification. This paper proposes a time-varying parameter (TVP) nonlinear FAVAR model: it is the first to embed a TVP-VAR into the FAVAR framework and introduces a grouped sparse autoencoder incorporating cross-category Spike-and-Slab Lasso priors, enabling semi-identifiability of factors with clear economic interpretations. The method integrates Bayesian inference with structured sparsity constraints. In U.S. macroeconomic forecasting, it substantially improves both point and density forecast accuracy. Impulse response analysis reveals that monetary policy shocks exert milder but more uncertain effects during recessions. This work establishes a new paradigm for nonlinear macroeconomic modeling—rigorous in theory and robust in empirical performance.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Probabilistic Circuits and Graphical Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Enhances interpretability in nonlinear FAVAR models.
• Integrates time-varying parameters for dynamic economic analysis.
• Improves forecasting accuracy with Grouped Sparse autoencoder.

Innovation

• Grouped Sparse autoencoder with Spike-and-Slab Lasso
• Time-varying parameters in VAR component
• Enhanced interpretability and forecasting performance