Macro-level modeling is still the dominant approach in many demographic applications because of its simplicity. Individual-level models, on the other hand, provide a more comprehensive understanding of observed patterns; however, their estimation using real data has remained a challenge. The approach we introduce in this article attempts to overcome this limitation. Using likelihood-free inference techniques, we show that it is possible to estimate the parameters of a simple but demographically interpretable individual-level model of the reproductive process from a set of aggregate fertility rates. By estimating individual-level quantities from widely available aggregate data, this approach can contribute to a better understanding of reproductive behavior and its driving mechanisms. It also allows for a more direct link between individual-level and population-level processes. We illustrate our approach using data from three natural fertility populations.

翻译：宏观层面建模因其简便性，仍是许多人口学应用中的主导方法。然而，个体层面模型能更全面地理解观察到的模式，但利用真实数据进行模型估计仍然是一项挑战。本文提出的方法试图克服这一局限性。通过使用无似然推断技术，我们证明可以从一组总和生育率数据中估计出一个简单但具有人口学可解释性的个体层面生育过程模型的参数。通过从广泛可得的汇总数据中估计个体层面参数，该方法有助于更深入地理解生育行为及其驱动机制，同时也使得个体层面与群体层面过程之间的关联更加直接。我们利用三个自然生育人群的数据展示了该方法的应用。