This paper empirically evaluates the potentially nonlinear nexus between financial indicators and the distribution of future GDP growth, using a rich set of macroeconomic and financial variables covering thirteen advanced economies. We evaluate the out-of-sample forecast performance of financial variables for GDP growth, including a fully real-time exercise based on a flexible nonparametric model. We also use a parametric model to estimate the moments of the time-varying distribution of GDP and evaluate their in-sample estimation uncertainty. Our overall conclusion is pessimistic: moments other than the conditional mean are poorly estimated, and no predictors we consider provide robust and precise advance warnings of tail risks or indeed about any features of the GDP growth distribution other than the mean. In particular, financial variables contribute little to such distributional forecasts, beyond the information contained in real indicators.

本文使用一组涵盖十三个发达经济体的丰富的宏观经济和金融变量，从经验上评估了财务指标与未来GDP增长分布之间的潜在非线性关系。我们评估了用于GDP增长的金融变量的样本外预测绩效，包括基于灵活非参数模型的完全实时演习。我们还使用参数模型来估算GDP随时间变化的时刻，并评估其样本内估算的不确定性。我们的总体结论是悲观的：除了条件均值以外的时刻均未得到很好的估计，我们认为没有任何预测因素能够提供有力而精确的提前预警，表明尾部风险或GDP增长分布的任何特征（均值除外）。尤其是，