Dynamic Factor Stochastic Volatility-in-Mean VAR for Large Macroeconomic Panels

TL;DR

This paper introduces a dynamic factor stochastic volatility-in-mean VAR model that captures macroeconomic uncertainty effects on large panels, improving forecast accuracy during crises.

Contribution

It develops a new high-dimensional VAR model with latent volatility factors and an efficient MCMC estimation method, enhancing macroeconomic forecasting during turbulent periods.

Findings

The model outperforms benchmarks in forecasting during the 2008 crisis.

Latent volatility factors effectively capture common movements in variances.

Allowing volatility in the mean improves understanding of macroeconomic dynamics.

Abstract

We develop a dynamic factor stochastic volatility-in-mean (SVM) specification for vector autoregressions (VARs) that embeds an SVM component within a dynamic factor stochastic volatility structure. A small number of latent volatility factors capture common movements in conditional variances, while volatility enters the conditional mean of the VAR. This specification allows time-varying uncertainty to influence macroeconomic dynamics through both second moments and expected outcomes while preserving tractability in large panels. We construct an efficient Markov chain Monte Carlo algorithm for estimation in this high-dimensional, non-Gaussian setting. Using quarterly data on twenty variables from the FRED-QD database, we compare predictive performance with the benchmark stochastic volatility VAR model. The dynamic factor SVM specification delivers superior forecasts for more variables during major macroeconomic disruptions such as the 2008 global financial crisis. The results indicate that allowing volatility to enter the mean captures an important transmission channel in macroeconomic dynamics.