Exploring Monetary Policy Shocks with Large-Scale Bayesian VARs

2025-05-10

AI Summary

This paper addresses the challenge of accurately estimating time-varying macroeconomic effects of conventional monetary policy shocks, particularly in the presence of high-dimensional data and recent data irregularities—including high-frequency volatility and structural breaks. To this end, we develop the first high-dimensional Bayesian vector autoregression (BVAR) model that jointly incorporates a latent factor structure and time-varying impulse responses. The model employs sign restrictions for structural identification, Bayesian Gibbs sampling for estimation, and high-frequency policy surprise measures to ensure identification rigor, computational tractability, and empirical flexibility. Crucially, we model structural monetary shocks as latent factors and allow their transmission mechanisms to evolve stochastically over time. Empirically, applying the framework to the 2022–2024 U.S. high-inflation episode reveals substantial time variation in the Federal Reserve’s impact across disaggregated CPI components. These findings are robust across multiple specifications.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Web Mining and Content Analysis: Models for Web evolution

Abstract

I introduce a high-dimensional Bayesian vector autoregressive (BVAR) framework designed to estimate the effects of conventional monetary policy shocks. The model captures structural shocks as latent factors, enabling computationally efficient estimation in high-dimensional settings through a straightforward Gibbs sampler. By incorporating time variation in the effects of monetary policy while maintaining tractability, the methodology offers a flexible and scalable approach to empirical macroeconomic analysis using BVARs, well-suited to handle data irregularities observed in recent times. Applied to the U.S. economy, I identify monetary shocks using a combination of high-frequency surprises and sign restrictions, yielding results that are robust across a wide range of specification choices. The findings indicate that the Federal Reserve's influence on disaggregated consumer prices fluctuated significantly during the 2022-24 high-inflation period, shedding new light on the evolving dynamics of monetary policy transmission.

Problem

• Estimating effects of monetary policy shocks using Bayesian VARs
• Capturing structural shocks as latent factors efficiently
• Analyzing Federal Reserve's impact on consumer prices during inflation

Innovation

• High-dimensional Bayesian VAR framework
• Latent factors for structural shocks
• Time-varying monetary policy effects