Robust Econometrics for Growth-at-Risk

TL;DR

This paper develops robust econometric methods for estimating Growth-at-Risk (GaR) tails, addressing limitations of previous approaches, and demonstrates improved predictive accuracy and insights into financial anomalies.

Contribution

It introduces a new theoretical framework for robust GaR tail estimation that outperforms existing methods in predictive accuracy.

Findings

Robust econometrics outperform existing methods in simulations.

Accurate long-term GaR predictions capturing financial anomalies.

Enhanced understanding of tail risks in economic growth.

Abstract

The Growth-at-Risk (GaR) framework has garnered attention in recent econometric literature, yet current approaches implicitly assume a constant Pareto exponent. We introduce novel and robust econometrics to estimate the tails of GaR based on a rigorous theoretical framework and establish validity and effectiveness. Simulations demonstrate consistent outperformance relative to existing alternatives in terms of predictive accuracy. We perform a long-term GaR analysis that provides accurate and insightful predictions, effectively capturing financial anomalies better than current methods.