Research

Dr Yizhou (Kyle) Kuang, University of Manchester

Outlier-robust Bayesian Vector Autoregressions (with Dimitris Korobills)
Friday, 25 September 2026. 15:00
Room 141 A, ASBS

Abstract

We develop Bayesian vector autoregressions (VARs) that are robust to outliers, drawing on ideas from empirical risk minimization rather than on detecting, deleting, or parametrically modeling outliers. Robustness is achieved from two complementary perspectives, a loss-based approach that replaces the Gaussian likelihood with a robust loss inside a generalized Bayesian update, and a conventional likelihood-based approach that embeds a smooth robust loss in an exact scale-mixture likelihood. Both approaches to Bayesian VAR inference discount aberrant observations automatically, and each comes with a dedicated, efficient estimation algorithm. We prove that robust loss learning bounds the influence of an outlier on coefficients and forecasts, whereas its effect under Gaussian learning grows with the size of the outlier. Forecasting U.S.\ macroeconomic variables through 2025Q2, the robust VARs cut one-quarter-ahead forecast errors by 16 to 18 percent during the COVID19 period at negligible cost in tranquil periods.

Bio

Yizhou (Kyle) Kuang is a Lecturer in Economics at the University of Manchester. His research focuses on Bayesian econometrics, partial identification, macroeconometrics, and computational methods. He received his PhD in Economics from Cornell University.

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First published: 15 September 2026