The accelerating shift toward low and ultra-low fertility has intensified the debate over whether countries now undergoing rapid decline are approaching stabilization or entering a more persistent low-fertility regime. Existing projection systems answer that question differently because they embed different assumptions about recovery and about the role of external drivers. To provide an empirical benchmark in this debate, we introduce NeuralTFR, an endogenous global forecasting framework based on a recurrent neural network. Drawing on a harmonized panel of historical fertility series from 196 countries and territories, the model pools cross-country information to learn demographic momentum and generate empirical prediction intervals via multi-quantile regression. Evaluated on a held-out period (2009--2023), NeuralTFR achieves lower point-forecast errors than a Naive Drift baseline and BayesTFR, the United Nations' Bayesian Hierarchical Model, while maintaining competitive uncertainty calibration. In forward projections to 2040, NeuralTFR points to broader exposure to low and very low fertility than BayesTFR, suggesting weaker support for near-term stabilization while still falling short of the most severe decline paths predicted by the Global Burden of Disease project.

翻译：低生育率和超低生育率的加速转变，加剧了关于当前经历快速下降的国家是趋于稳定还是进入更持久低生育率模式的争论。现有预测系统因对恢复能力和外部驱动因素作用的不同假设而对此问题给出不同答案。为在这场辩论中提供实证基准，我们提出了NeuralTFR——一种基于循环神经网络的内生性全球预测框架。该模型利用来自196个国家和地区的历史生育率面板数据，通过整合跨国信息学习人口惯性，并采用多分位数回归生成经验预测区间。在留出区间（2009-2023年）评估中，NeuralTFR相比于Naive Drift基线和联合国贝叶斯层次模型BayesTFR，实现了更低的点预测误差，同时保持具有竞争力的不确定性校准能力。在面向2040年的前瞻性预测中，NeuralTFR显示低生育率和极低生育率的暴露范围比BayesTFR更广，表明近期稳定化的支撑较弱，但仍未达到全球疾病负担项目预测的最严重下降路径。