Probabilistic Estimation and Projection of the Annual Total Fertility Rate Accounting for Past Uncertainty: A Major Update of the bayesTFR R Package

TL;DR

This paper introduces an updated bayesTFR R package that improves probabilistic total fertility rate projections by incorporating past estimation uncertainty and extending to annual data with autoregressive modeling.

Contribution

The update adds a new extension for past uncertainty, enables annual TFR projections, and introduces an autoregressive component for better autocorrelation modeling.

Findings

Enhanced model accuracy in fertility projections

Effective handling of annual TFR data

Improved tools for visualization and diagnostics

Abstract

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.