论文标题
过去不确定性的年度总生育率核算的概率估计和投影：Bayestfr R包的重大更新
Probabilistic Estimation and Projection of the Annual Total Fertility Rate Accounting for Past Uncertainty: A Major Update of the bayesTFR R Package
论文作者
论文摘要
R的bayestfr套件提供了一组功能，以生成所有国家的总生育率（TFR）的概率预测，并被广泛使用，包括作为联合国所有国家官方人口预测的基础的一部分。 Liu和Raftery（2020）通过添加一个解释过去TFR估计不确定性的层来扩展理论模型。 Bayestfr的重大更新实现了新的扩展名。此外，生产年度TFR估计和预测的一项新功能扩展了五年时间段内估算和预测的现有功能。为了说明该模型年度版本中较大的自相关性，已经开发了一个额外的自回归组件。本文总结了更新的模型，描述了在不同设置下生成概率估计和预测的基本步骤，比较性能，并提供有关如何汇总，可视化和诊断模型结果的说明。
The bayesTFR package for R provides a set of functions to produce probabilistic projections of the total fertility rates (TFR) for all countries, and is widely used, including as part of the basis for the UN's official population projections for all countries. Liu and Raftery (2020) extended the theoretical model by adding a layer that accounts for the past TFR estimation uncertainty. A major update of bayesTFR implements the new extension. Moreover, a new feature of producing annual TFR estimation and projections extends the existing functionality of estimating and projecting for five-year time periods. An additional autoregressive component has been developed in order to account for the larger autocorrelation in the annual version of the model. This article summarizes the updated model, describes the basic steps to generate probabilistic estimation and projections under different settings, compares performance, and provides instructions on how to summarize, visualize and diagnose the model results.
