Exploring Monetary Policy Shocks with Large-Scale Bayesian VARs

TL;DR

This paper presents a scalable Bayesian VAR framework that efficiently estimates the effects of monetary policy shocks, capturing time variation and structural shocks as latent factors, with robust results applied to the U.S. economy during recent high-inflation years.

Contribution

It introduces a high-dimensional Bayesian VAR model with latent structural shocks and a Gibbs sampler, enabling flexible, scalable analysis of monetary policy impacts in macroeconomic data.

Findings

Federal Reserve's influence on consumer prices varied during 2022-24

Method provides robust identification of monetary shocks

Model handles data irregularities effectively

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.