2 papers
econ.EM2026
Robust Econometrics for Growth-at-Risk
Tobias Adrian, Yuya Sasaki, Yulong Wang
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…
econ.GN2025
Machine-learning Growth at Risk
Tobias Adrian, Hongqi Chen, Max-Sebastian Dovì +1
We analyse growth vulnerabilities in the US using quantile partial correlation regression, a selection-based machine-learning method that achieves model selection consistency under…