Origins and Nature of Macroeconomic Instability in Vector Autoregressions

TL;DR

This paper demonstrates that non-linearity causes time-varying parameters in macroeconomic VAR models and shows that a few common shocks drive most macroeconomic instabilities, improving forecasting accuracy.

Contribution

It establishes the link between non-linearity and TVPs, and introduces a factor-structured TVP-VAR model that captures macroeconomic and financial instabilities more effectively.

Findings

Most instabilities are driven by a few common factors.

The factor-structured TVP-VAR improves forecast accuracy.

TVPs are primarily influenced by structural shocks, not just structural instability.

Abstract

For a general class of dynamic and stochastic structural models, we show that (i) non-linearity in economic dynamics is a necessary and sufficient condition for time-varying parameters (TVPs) in the reduced-form VARMA process followed by observables, and (ii) all parameters' time-variation is driven by the same, typically few sources of stochasticity: the structural shocks. Our results call into question the common interpretation that TVPs are due to "structural instabilities". Motivated by our theoretical analysis, we model a set of macroeconomic and financial variables as a TVP-VAR with a factor-structure in TVPs. This reveals that most instabilities are driven by a few factors, which comove strongly with measures of macroeconomic uncertainty and the contribution of finance to real economic activity, commonly emphasized as important sources of non-linearities in macroeconomics. Furthermore, our model yields improved forecasts relative to the standard TVP-VAR where TVPs evolve as independent random walks.