Over the last 40 years, various frameworks have been proposed to model economic and financial variables relevant to actuaries. These models are helpful, but searching for a unique model that gives optimal forecasting performance can be frustrating and ultimately futile. This study therefore investigates whether we can create better, more reliable economic scenario generators by combining them. We first consider eight prominent economic scenario generators and apply Bayesian estimation techniques to them, thus allowing us to account for parameter uncertainty. We then rely on predictive distribution stacking to obtain optimal model weights that prescribe how the models should be averaged. The weights are constructed in a leave-future-out fashion to build truly out-of-sample forecasts. An extensive empirical study based on three economies—the United States, Canada, and the United Kingdom—and data from 1992 to 2021 is performed. We find that the optimal weights change over time and differ from one economy to another. The out-of-sample behavior of the ensemble model compares favorably to the other eight models: the ensemble model’s performance is substantially better than that of the worse models and comparable to that of the better models. Creating ensembles is thus beneficial from an out-of-sample perspective because it allows for robust and reasonable forecasts.

在过去的 40 年里，人们提出了各种框架来对与精算师相关的经济和金融变量进行建模。这些模型很有帮助，但寻找一种能够提供最佳预测性能的独特模型可能会令人沮丧，并最终徒劳无功。因此，本研究探讨了我们是否可以通过将它们结合起来创建更好、更可靠的经济情景生成器。我们首先考虑八个著名的经济情景生成器，并对它们应用贝叶斯估计技术，从而使我们能够解释参数的不确定性。然后，我们依靠预测分布叠加来获得最佳模型权重，该权重规定了如何对模型进行平均。权重是在留有未来的情况下构建的建立真正的样本外预测的时尚。基于美国、加拿大和英国这三个经济体以及 1992 年至 2021 年的数据进行了广泛的实证研究。我们发现最佳权重随着时间的推移而变化，并且因经济体而异。集成模型的样本外行为优于其他八个模型：集成模型的性能明显优于较差模型，并且与较好模型相当。因此，从样本外的角度来看，创建集成是有益的，因为它可以进行稳健且合理的预测。