Models can be wrong and recognising their limitations is important in financial and economic decision making under uncertainty. Robust strategies, which are least sensitive to perturbations of the underlying model, take uncertainty into account. Interpreting the explicit set of alternative models surrounding the baseline model has been difficult so far. We specify alternative models via a time-consistent set of equivalent probability measures and derive a quantitative bound on the uncertainty set. We find an explicit ex ante relation between the size of this set, and the Type I and II error probabilities on the statistical test that is hypothetically performed to investigate whether the alternative model specification could be rejected at a future test horizon. The hypothetical test is constructed to obtain all alternative models that are indistinguishable from the baseline model. We also link the ambiguity bound, which is now a function of interpretable variables, to numerical values on several divergence measures and illustrate our methodology on a robust investment problem.

模型可能是错误的，认识到其局限性对于不确定性下的金融和经济决策至关重要。对基本模型的扰动最不敏感的稳健策略会考虑不确定性。到目前为止，很难解释围绕基线模型的一组明确的替代模型。我们通过时间一致的等效概率度量集指定替代模型，并得出不确定性集的定量边界。我们发现此数据集的大小与统计测试中的I型和II型错误概率之间存在显着的事前关系，假设此关系是为了研究在将来的测试范围内是否可以拒绝替代模型规格而进行的。构建假设检验以获得与基准模型没有区别的所有替代模型。我们还将不确定性界限（现在是可解释变量的函数）与几种差异度量的数值联系起来，并说明了关于稳健投资问题的方法。