We introduce a new regression method that relates the mean of an outcome variable to covariates, given the "adverse condition" that a distress variable falls in its tail. This allows to tailor classical mean regressions to adverse economic scenarios, which receive increasing interest in managing macroeconomic and financial risks, among many others. In the terminology of the systemic risk literature, our method can be interpreted as a regression for the Marginal Expected Shortfall. We propose a two-step procedure to estimate the new models, show consistency and asymptotic normality of the estimator, and propose feasible inference under weak conditions allowing for cross-sectional and time series applications. The accuracy of the asymptotic approximations of the two-step estimator is verified in simulations. Two empirical applications show that our regressions under adverse conditions are valuable in such diverse fields as the study of the relation between systemic risk and asset price bubbles, and dissecting macroeconomic growth vulnerabilities into individual components.

翻译：本文提出了一种新的回归方法，该方法在"极端条件"（即压力变量处于其尾部）下，建立结果变量的均值与协变量之间的关系。这使得经典均值回归能够适用于极端经济情景，在宏观经济与金融风险管理等诸多领域日益受到关注。从系统性风险研究的术语体系来看，本方法可解释为边际期望损失回归。我们提出了估计该新模型的两步程序，证明了估计量的一致性与渐近正态性，并在允许横截面与时间序列应用的弱条件下提出了可行的推断方法。通过模拟实验验证了两步估计量渐近近似的准确性。两项实证应用表明，我们的极端条件回归在系统性风险与资产价格泡沫关系研究、以及宏观经济增长脆弱性的构成要素解析等不同领域均具有重要价值。