The addition of a set of cohort parameters to a mortality model can generate complex identifiability issues due to the collinearity between the dimensions of age, period and cohort. These issues can lead to robustness problems and difficulties making projections of future mortality rates. Since many modern mortality models incorporate cohort parameters, we believe that a comprehensive analysis of the identifiability issues in age/period/cohort mortality models is needed. In this paper, we discuss the origin of identifiability issues in general models before applying these insights to simple but commonly used mortality models. We then discuss how to project mortality models so that our forecasts of the future are independent of any arbitrary choices we make when fitting a model to data in order to identify the historical parameters.

中文翻译：

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年龄/时期/队列死亡率模型中的可识别性

由于年龄、时期和队列维度之间的共线性，将一组队列参数添加到死亡率模型可能会产生复杂的可识别性问题。这些问题可能导致稳健性问题和难以预测未来死亡率。由于许多现代死亡率模型包含队列参数，我们认为需要对年龄/时期/队列死亡率模型中的可识别性问题进行全面分析。在本文中，我们讨论了一般模型中可识别性问题的起源，然后将这些见解应用于简单但常用的死亡率模型。然后，我们讨论如何预测死亡率模型，以便我们对未来的预测独立于我们在将模型拟合到数据以识别历史参数时所做的任何任意选择。