Learning from crises: A new class of time-varying parameter VARs with observable adaptation

TL;DR

This paper introduces an adaptive VAR model that uses observable macroeconomic indicators to more effectively capture abrupt shifts during crises, improving forecast accuracy.

Contribution

It proposes a new AVP-VAR model that replaces latent states with observable variables, simplifying estimation and enhancing adaptability during crises.

Findings

AVP-VAR outperforms traditional TVPs in forecasting accuracy.

Adaptive parameters are more parsimonious and flexible.

Model performs well during periods of high volatility.

Abstract

We revisit macroeconomic time-varying parameter vector autoregressions (TVP-VARs), whose persistent coefficients may adapt too slowly to large, abrupt shifts such as those during major crises. We explore the performance of an adaptively-varying parameter (AVP) VAR that incorporates deterministic adjustments driven by observable exogenous variables, replacing latent state innovations with linear combinations of macroeconomic and financial indicators. This reformulation collapses the state equation into the measurement equation, enabling simple linear estimation of the model. Simulations show that adaptive parameters are substantially more parsimonious than conventional TVPs, effectively disciplining parameter dynamics without sacrificing flexibility. Using macroeconomic datasets for both the U.S. and the euro area, we demonstrate that AVP-VAR consistently improves out-of-sample forecasts, especially during periods of heightened volatility.